{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2d8be6af",
   "metadata": {},
   "source": [
    "# 25 — L'effet de page, une fois la composition retirée\n",
    "\n",
    "Le [chapitre précédent](../format-et-retour.md) a mesuré qu'un format enrichi servi plus haut\n",
    "retire **8,4 points** d'examen à ce qui suit, à rang et à hauteur de page comparables, et en a\n",
    "tiré une conclusion large : la remise d'attention $w_R$ ne serait pas une propriété du rang mais\n",
    "de la **page servie**, et aucune loi $e(R)$ ne pourrait la représenter.\n",
    "\n",
    "Ce chapitre reprend la même mesure avec un contrôle que le précédent n'avait pas : **le contenu**.\n",
    "\n",
    "Comparer les pages enrichies aux autres, même à rang égal, c'est comparer deux populations de\n",
    "requêtes. Un encadré de réponse ne s'affiche pas au hasard : il répond à une question factuelle,\n",
    "qui n'appelle pas la même lecture qu'une recherche exploratoire. L'écart mesuré peut donc venir\n",
    "du format, ou de *ce sur quoi* le format apparaît.\n",
    "\n",
    "La question de ce chapitre est celle-là, et une seule : **quelle part de l'effet publié est un\n",
    "effet, et quelle part est une composition ?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4926449b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T16:01:23.558318Z",
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     "iopub.status.idle": "2026-08-23T16:01:24.179619Z",
     "shell.execute_reply": "2026-08-23T16:01:24.179122Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e(R | nombre de formats enrichis servis plus haut)\n",
      "\n",
      "rang         0        1        2        3        4\n",
      "   1    0.984        .        .        .        .\n",
      "   2    0.913    0.833        .        .        .\n",
      "   3    0.722    0.606    0.590        .        .\n",
      "   4    0.507    0.415    0.405    0.446        .\n",
      "   5    0.372    0.302    0.267    0.312    0.341\n",
      "   6    0.323    0.270    0.227    0.219    0.259\n",
      "   7    0.290    0.240    0.199    0.187    0.205\n",
      "   8    0.277    0.218    0.175    0.172    0.174\n",
      "   9    0.251    0.195    0.156    0.151    0.149\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from ide.entropy import label_diversity_index\n",
    "from ide.exposure import load_digest, page_effect_counts, page_examination_curve\n",
    "from ide.logs import Impressions, count_above, stratified_risk_ratio\n",
    "from ide.plotting import PALETTE, save_figure, use_project_style\n",
    "\n",
    "use_project_style()\n",
    "DEPTH = 9  # au-delà, Baidu-ULTR ne sert plus qu'une poignée d'impressions par rang\n",
    "\n",
    "# Le journal brut n'est pas versionné : le condensé porte les comptes appariés, et rend les\n",
    "# mêmes chiffres que la lecture directe du fichier — deux tests le vérifient.\n",
    "digest = load_digest()\n",
    "curve = page_examination_curve(digest)\n",
    "exposures = curve[\"exposures\"].reshape(-1, 5).astype(float)\n",
    "seen = curve[\"seen\"].reshape(-1, 5).astype(float)\n",
    "ranks = np.arange(1, exposures.shape[0] + 1)\n",
    "\n",
    "print(\"e(R | nombre de formats enrichis servis plus haut)\\n\")\n",
    "print(\"rang \" + \"\".join(f\"{n:>9}\" for n in range(5)))\n",
    "for rank in range(DEPTH):\n",
    "    line = \"\".join(f\"{seen[rank, n] / exposures[rank, n]:>9.3f}\"\n",
    "                   if exposures[rank, n] > 500 else f\"{'.':>9}\" for n in range(5))\n",
    "    print(f\"{ranks[rank]:>4}\" + line)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ccbfe2",
   "metadata": {},
   "source": [
    "## 1. Ce que dit le contraste brut\n",
    "\n",
    "Trois façons de lire le même tableau, de la plus naïve à la plus prudente. Aucune des trois ne\n",
    "tient encore le contenu fixe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "339676ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T16:01:24.180421Z",
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     "shell.execute_reply": "2026-08-23T16:01:24.182849Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contraste brut, rangs 2 à 9          RR = 0.613\n",
      "stratifié par rang                  RR = 0.817  IC95 = [0.812  0.823]  (452 689 impressions)\n",
      "\n",
      "C'est ce second chiffre que le chapitre précédent publiait : dix-huit points relatifs,\n",
      "soit les huit points d'examen perdus sur une base de 0,45.\n"
     ]
    }
   ],
   "source": [
    "shallow = (ranks >= 2) & (ranks <= DEPTH)\n",
    "untreated, treated = exposures[:, 0], exposures[:, 1:].sum(1)\n",
    "untreated_seen, treated_seen = seen[:, 0], seen[:, 1:].sum(1)\n",
    "\n",
    "brut = ((treated_seen[shallow].sum() / treated[shallow].sum())\n",
    "        / (untreated_seen[shallow].sum() / untreated[shallow].sum()))\n",
    "par_rang = stratified_risk_ratio(untreated[shallow], treated[shallow],\n",
    "                                 untreated_seen[shallow], treated_seen[shallow])\n",
    "\n",
    "print(f\"contraste brut, rangs 2 à {DEPTH}          RR = {brut:.3f}\")\n",
    "print(f\"stratifié par rang                  RR = {par_rang.ratio:.3f}  \"\n",
    "      f\"IC95 = [{par_rang.low:.3f}, {par_rang.high:.3f}]  \"\n",
    "      f\"({par_rang.impressions:,} impressions)\".replace(\",\", \" \"))\n",
    "print(\"\\nC'est ce second chiffre que le chapitre précédent publiait : dix-huit points relatifs,\")\n",
    "print(\"soit les huit points d'examen perdus sur une base de 0,45.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4554f81a",
   "metadata": {},
   "source": [
    "## 2. Le contrôle qui décide de la suite\n",
    "\n",
    "Règle du dépôt, acquise trois fois à ses dépens : **appliquer d'abord le protocole à des données\n",
    "dont on connaît la réponse.**\n",
    "\n",
    "On simule ici un monde où le format n'a *aucun* effet sur l'examen, mais où les pages enrichies\n",
    "tombent sur des contenus systématiquement moins consultés — exactement le confondant soupçonné.\n",
    "Un estimateur qui prétend mesurer l'effet du format doit rendre 1.\n",
    "\n",
    "On simule ensuite un monde où le format a un effet **connu** de $0{,}90$. Le même estimateur doit\n",
    "le retrouver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a860021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T16:01:24.183912Z",
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     "iopub.status.idle": "2026-08-23T16:01:25.369016Z",
     "shell.execute_reply": "2026-08-23T16:01:25.368584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "effet simulé   RR stratifié par rang   RR à contenu fixé\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1.00              0.785                1.005  IC95 = [0.993, 1.016]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0.90              0.701                0.896  IC95 = [0.886, 0.907]\n"
     ]
    }
   ],
   "source": [
    "CATALOGUE, FEEDS, SLOTS = 300, 120_000, DEPTH\n",
    "\n",
    "\n",
    "def monde_simule(effet, rng, severity=0.9):\n",
    "    # Un journal où l'attribution du format dépend de la qualité des contenus servis.\n",
    "    quality = np.clip(rng.lognormal(-0.9, 0.5, CATALOGUE), 0.02, 1.0)\n",
    "    items = rng.integers(0, CATALOGUE, size=(FEEDS, SLOTS))\n",
    "    rank_grid = np.tile(np.arange(1, SLOTS + 1), (FEEDS, 1))\n",
    "\n",
    "    # Le format enrichi tombe sur les fils dont les contenus sont les moins consultés : la\n",
    "    # composition, à elle seule, suffit à creuser un écart.\n",
    "    enrichi = quality[items].mean(1) < np.quantile(quality[items].mean(1), 0.5)\n",
    "    au_dessus = np.where(enrichi[:, None] & (rank_grid >= 2), 1, 0)\n",
    "\n",
    "    probability = np.clip(quality[items] * rank_grid ** -severity, 0.0, 1.0)\n",
    "    probability = np.where(au_dessus > 0, probability * effet, probability)\n",
    "    examine = (rng.random((FEEDS, SLOTS)) < probability).astype(float)\n",
    "\n",
    "    journal = Impressions(items=items.ravel(), ranks=rank_grid.ravel(),\n",
    "                          clicks=np.zeros(FEEDS * SLOTS),\n",
    "                          feeds=np.repeat(np.arange(FEEDS), SLOTS),\n",
    "                          feed_lengths=np.full(FEEDS, SLOTS))\n",
    "    return journal, examine.ravel(), au_dessus.ravel()\n",
    "\n",
    "\n",
    "def apparie(journal, examine, traite, garde):\n",
    "    # Le rapport de risques, à contenu et rang fixés — le même code que sur données réelles.\n",
    "    strata = np.stack([journal.items[garde], journal.ranks[garde]], axis=1)\n",
    "    keys, index = np.unique(strata, axis=0, return_inverse=True)\n",
    "    slots = index * 2 + traite[garde]\n",
    "    counts = np.bincount(slots, minlength=2 * len(keys)).reshape(-1, 2).astype(float)\n",
    "    hits = np.bincount(slots, weights=examine[garde], minlength=2 * len(keys)).reshape(-1, 2)\n",
    "    return stratified_risk_ratio(counts[:, 0], counts[:, 1], hits[:, 0], hits[:, 1])\n",
    "\n",
    "\n",
    "generator = np.random.default_rng(20260823)\n",
    "print(\"effet simulé   RR stratifié par rang   RR à contenu fixé\")\n",
    "for effet in (1.0, 0.90):\n",
    "    journal, examine, au_dessus = monde_simule(effet, generator)\n",
    "    garde = journal.ranks >= 2\n",
    "    traite = (au_dessus > 0).astype(np.int64)\n",
    "\n",
    "    par_rang_simule = stratified_risk_ratio(\n",
    "        *[np.bincount(journal.ranks[garde], weights=poids)[2:]\n",
    "          for poids in ((traite[garde] == 0), (traite[garde] == 1),\n",
    "                        examine[garde] * (traite[garde] == 0),\n",
    "                        examine[garde] * (traite[garde] == 1))])\n",
    "    mesure = apparie(journal, examine, traite, garde)\n",
    "    print(f\"    {effet:.2f}              {par_rang_simule.ratio:.3f}                \"\n",
    "          f\"{mesure.ratio:.3f}  IC95 = [{mesure.low:.3f}, {mesure.high:.3f}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3bc9b75",
   "metadata": {},
   "source": [
    "Le protocole passe les deux épreuves : il ne voit rien là où il n'y a rien, et retrouve l'effet\n",
    "là où il est. La stratification par le rang seul, elle, échoue à la première — elle lit un effet\n",
    "franc dans un monde qui n'en porte aucun.\n",
    "\n",
    "C'est précisément la lecture que le chapitre précédent avait faite.\n",
    "\n",
    "## 3. La mesure, à contenu fixé puis à requête fixée\n",
    "\n",
    "Deux appariements indépendants. Le premier compare **le même document au même rang**, servi une\n",
    "fois avec un format enrichi au-dessus et une fois sans. Le second compare **la même requête au\n",
    "même rang**. Ils ne partagent ni leurs strates ni leurs impressions ; ils doivent néanmoins\n",
    "rendre le même chiffre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77baca6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-23T16:01:25.369883Z",
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     "shell.execute_reply": "2026-08-23T16:01:25.373132Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "même document  même rang   RR = 0.940  IC95 = [0.916  0.964]   1106 strates   31 045 impressions\n",
      "même requête  même rang    RR = 0.942  IC95 = [0.869  1.022]    880 strates    4 261 impressions\n",
      "\n",
      "rang   strates          RR      IC95\n",
      "   2       163       0.983   [0.947, 1.020]\n",
      "   3       163       0.964   [0.938, 0.990]\n",
      "   4       152       0.822   [0.740, 0.914]\n",
      "   5       140       0.851   [0.745, 0.973]\n",
      "   6       138       0.860   [0.689, 1.073]\n",
      "   7       130       0.977   [0.842, 1.134]\n",
      "   8       109       0.949   [0.635, 1.419]\n",
      "   9       111       1.203   [0.735, 1.970]\n"
     ]
    }
   ],
   "source": [
    "def rapport(stratification, borne=DEPTH):\n",
    "    counts = page_effect_counts(digest, stratification)\n",
    "    kept = (counts[\"ranks\"] >= 2) & (counts[\"ranks\"] <= borne)\n",
    "    return stratified_risk_ratio(counts[\"untreated\"][kept], counts[\"treated\"][kept],\n",
    "                                 counts[\"untreated_seen\"][kept], counts[\"treated_seen\"][kept])\n",
    "\n",
    "\n",
    "par_contenu, par_requete = rapport(\"document\"), rapport(\"query\")\n",
    "for nom, mesure in ((\"même document, même rang\", par_contenu),\n",
    "                    (\"même requête, même rang\", par_requete)):\n",
    "    print(f\"{nom:<26} RR = {mesure.ratio:.3f}  IC95 = [{mesure.low:.3f}, {mesure.high:.3f}]  \"\n",
    "          f\"{mesure.strata:>5} strates, {mesure.impressions:>7,} impressions\".replace(\",\", \" \"))\n",
    "\n",
    "print(\"\\nrang   strates          RR      IC95\")\n",
    "counts = page_effect_counts(digest, \"document\")\n",
    "par_rang_apparie = {}\n",
    "for rank in range(2, DEPTH + 1):\n",
    "    cell = counts[\"ranks\"] == rank\n",
    "    mesure = stratified_risk_ratio(counts[\"untreated\"][cell], counts[\"treated\"][cell],\n",
    "                                   counts[\"untreated_seen\"][cell], counts[\"treated_seen\"][cell])\n",
    "    par_rang_apparie[rank] = mesure\n",
    "    print(f\"{rank:>4}   {mesure.strata:>7}   {mesure.ratio:>9.3f}   \"\n",
    "          f\"[{mesure.low:.3f}, {mesure.high:.3f}]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66708946",
   "metadata": {},
   "source": [
    "**Les deux tiers de l'effet publié étaient de la composition.** Dix-huit points relatifs\n",
    "deviennent six, et les deux appariements concordent à deux millièmes près alors que l'un repose\n",
    "sur sept fois plus d'impressions que l'autre.\n",
    "\n",
    "Ce qui reste est **établi** par l'appariement le plus peuplé — l'intervalle exclut 1 — et ne\n",
    "l'est pas par l'autre, trop peu peuplé pour trancher seul. Rang par rang, aucune tendance ne se\n",
    "dégage : l'effet ne se creuse pas avec la profondeur, et l'hypothèse la plus simple qui reste est\n",
    "celle d'un **facteur constant**.\n",
    "\n",
    "## 4. Ce que coûte, en pratique, d'ignorer la page\n",
    "\n",
    "La conclusion du chapitre précédent — « aucune loi $e(R)$ ne peut représenter cela » — se teste\n",
    "en la chiffrant. Deux écarts, sur les mêmes fils simulés :\n",
    "\n",
    "* celui qu'on commet en employant la remise **marginale** mesurée là où la page est enrichie ;\n",
    "* celui qu'on commet en employant la **convention $1/R$**, qui est ce que fait la littérature — et\n",
    "  ce que faisait ce dépôt jusqu'au chapitre 23."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23a516d7",
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    "execution": {
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     "shell.execute_reply": "2026-08-23T16:01:50.873600Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "écart d'indice, page ignorée      médian 0.0025   99e centile 0.0066\n",
      "écart d'indice, convention 1/R    médian 0.0350   99e centile 0.0735\n",
      "\n",
      "rapport des deux : 14 fois\n",
      "\n",
      "sévérité mesurée en ignorant la page      η = 0.876\n",
      "sévérité sur la page contrefactuelle nue  η = 0.854\n"
     ]
    }
   ],
   "source": [
    "part_enrichie = (exposures[:, 1:].sum(1) / exposures.sum(1))[:DEPTH]\n",
    "marginale = (seen.sum(1) / exposures.sum(1))[:DEPTH]\n",
    "\n",
    "# La remise que verrait un régulateur mesurant sans distinguer les pages, contre les deux\n",
    "# compositions extrêmes : une page entièrement nue, une page enrichie dès le premier rang. Le\n",
    "# rang 1 ne porte jamais rien au-dessus de lui : c'est par lui que l'effet cesse d'être une\n",
    "# simple homothétie, à laquelle un indice normalisé serait insensible.\n",
    "nue = marginale / (1 - part_enrichie + part_enrichie * par_contenu.ratio)\n",
    "enrichie = nue.copy()\n",
    "enrichie[1:] *= par_contenu.ratio\n",
    "convention = np.arange(1, DEPTH + 1, dtype=float) ** -1.0\n",
    "\n",
    "\n",
    "def indice_expose(poids, etiquettes, catalogue=4):\n",
    "    # L'IDE d'un fil : l'entropie des points de vue servis, pondérée par l'attention.\n",
    "    servi = np.array([poids[etiquettes == vue].sum() for vue in range(catalogue)])\n",
    "    parts = np.round(servi / servi.sum() * 10_000).astype(int)\n",
    "    return label_diversity_index(np.repeat(np.arange(catalogue), parts),\n",
    "                                 catalogue_size=catalogue)\n",
    "\n",
    "\n",
    "generator = np.random.default_rng(20260823)\n",
    "ecart_page, ecart_convention = [], []\n",
    "for _ in range(4_000):\n",
    "    etiquettes = generator.integers(0, 4, size=DEPTH)\n",
    "    reference = indice_expose(marginale, etiquettes)\n",
    "    ecart_page.append(max(abs(reference - indice_expose(enrichie, etiquettes)),\n",
    "                          abs(reference - indice_expose(nue, etiquettes))))\n",
    "    ecart_convention.append(abs(reference - indice_expose(convention, etiquettes)))\n",
    "ecart_page, ecart_convention = np.array(ecart_page), np.array(ecart_convention)\n",
    "\n",
    "print(f\"écart d'indice, page ignorée      médian {np.median(ecart_page):.4f}   \"\n",
    "      f\"99e centile {np.quantile(ecart_page, 0.99):.4f}\")\n",
    "print(f\"écart d'indice, convention 1/R    médian {np.median(ecart_convention):.4f}   \"\n",
    "      f\"99e centile {np.quantile(ecart_convention, 0.99):.4f}\")\n",
    "print(f\"\\nrapport des deux : {np.median(ecart_convention) / np.median(ecart_page):.0f} fois\")\n",
    "\n",
    "\n",
    "def severite(taux):\n",
    "    pente, _ = np.polyfit(np.log(np.arange(1, DEPTH + 1)), np.log(taux), 1)\n",
    "    return -pente\n",
    "\n",
    "\n",
    "print(f\"\\nsévérité mesurée en ignorant la page      η = {severite(marginale):.3f}\")\n",
    "print(f\"sévérité sur la page contrefactuelle nue  η = {severite(nue):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279b4505",
   "metadata": {},
   "source": [
    "## 5. La figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6edba811",
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     "shell.execute_reply": "2026-08-23T16:01:51.286422Z"
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1320x960 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2, 2, figsize=(11, 8))\n",
    "\n",
    "ax = axes[0, 0]\n",
    "for n in range(3):\n",
    "    lisible = exposures[:DEPTH, n] > 500\n",
    "    taux = np.divide(seen[:DEPTH, n], exposures[:DEPTH, n],\n",
    "                     out=np.zeros(DEPTH), where=exposures[:DEPTH, n] > 0)\n",
    "    ax.plot(ranks[:DEPTH][lisible], taux[lisible],\n",
    "            marker=\"o\", markersize=4,\n",
    "            color=[PALETTE[\"order\"], PALETTE[\"field\"], PALETTE[\"disorder\"]][n],\n",
    "            label=f\"{n} format enrichi au-dessus\" if n < 2 else f\"{n} formats enrichis\")\n",
    "ax.set(xlabel=\"rang servi\", ylabel=\"part des impressions examinées\",\n",
    "       title=\"La description : l'écart est net…\")\n",
    "ax.legend()\n",
    "\n",
    "ax = axes[0, 1]\n",
    "lectures = [(\"contraste brut\", brut, None, None, PALETTE[\"disorder\"]),\n",
    "            (\"stratifié par rang\", par_rang.ratio, par_rang.low, par_rang.high, PALETTE[\"field\"]),\n",
    "            (\"même requête, même rang\", par_requete.ratio, par_requete.low, par_requete.high,\n",
    "             PALETTE[\"neutral\"]),\n",
    "            (\"même document, même rang\", par_contenu.ratio, par_contenu.low, par_contenu.high,\n",
    "             PALETTE[\"remedy\"])]\n",
    "for position, (nom, valeur, bas, haut, couleur) in enumerate(lectures):\n",
    "    if bas is not None:\n",
    "        ax.plot([bas, haut], [position, position], color=couleur, linewidth=2.5)\n",
    "    ax.plot([valeur], [position], marker=\"D\", markersize=7, color=couleur)\n",
    "ax.axvline(1.0, color=PALETTE[\"neutral\"], linestyle=\":\", linewidth=1.2)\n",
    "ax.set(yticks=range(len(lectures)), yticklabels=[nom for nom, *_ in lectures],\n",
    "       xlabel=\"rapport de risques sur l'examen\", xlim=(0.55, 1.05),\n",
    "       title=\"…et il fond quand le contenu est tenu fixe\")\n",
    "ax.grid(axis=\"y\", alpha=0.0)\n",
    "\n",
    "ax = axes[1, 0]\n",
    "positions = sorted(par_rang_apparie)\n",
    "valeurs = [par_rang_apparie[rank].ratio for rank in positions]\n",
    "ax.errorbar(positions, valeurs,\n",
    "            yerr=[[v - par_rang_apparie[r].low for v, r in zip(valeurs, positions, strict=True)],\n",
    "                  [par_rang_apparie[r].high - v for v, r in zip(valeurs, positions, strict=True)]],\n",
    "            fmt=\"o\", color=PALETTE[\"remedy\"], ecolor=PALETTE[\"neutral\"], capsize=3)\n",
    "ax.axhline(1.0, color=PALETTE[\"neutral\"], linestyle=\":\", linewidth=1.2)\n",
    "ax.axhline(par_contenu.ratio, color=PALETTE[\"remedy\"], linestyle=\"--\", linewidth=1.2,\n",
    "           label=f\"facteur constant, {par_contenu.ratio:.2f}\")\n",
    "ax.set(xlabel=\"rang servi\", ylabel=\"rapport de risques\",\n",
    "       title=\"Aucune tendance avec la profondeur\")\n",
    "ax.legend()\n",
    "\n",
    "ax = axes[1, 1]\n",
    "ax.hist(ecart_page, bins=40, color=PALETTE[\"remedy\"], alpha=0.85,\n",
    "        label=f\"composition de page (médiane {np.median(ecart_page):.4f})\")\n",
    "ax.hist(ecart_convention, bins=40, color=PALETTE[\"disorder\"], alpha=0.7,\n",
    "        label=f\"convention 1/R (médiane {np.median(ecart_convention):.4f})\")\n",
    "ax.set(xlabel=\"écart d'indice sur un fil de neuf rangs\", ylabel=\"fils simulés\",\n",
    "       title=\"Ce qui fausse l'indice n'est pas la page\")\n",
    "ax.legend()\n",
    "\n",
    "save_figure(figure, \"fig25_effet_de_page.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e71bef",
   "metadata": {},
   "source": [
    "## 6. Ce que ce chapitre change\n",
    "\n",
    "**Une conclusion publiée par ce dépôt est corrigée d'un facteur trois.** L'effet du format sur\n",
    "l'examen n'est pas de dix-huit points relatifs mais de **six**, et les deux tiers de ce qui était\n",
    "publié tenaient à *quelles* requêtes déclenchent un encadré de réponse. L'erreur était de celles\n",
    "que ce dépôt a déjà rencontrées cinq fois : un chiffre du bon signe, du bon ordre de grandeur, et\n",
    "faux par ce qu'on n'avait pas tenu fixe.\n",
    "\n",
    "**L'affirmation la plus large du chapitre précédent tombe.** « Aucune loi $e(R)$ ne peut\n",
    "représenter cela » supposait un effet de page assez fort pour interdire une loi de rang. Une fois\n",
    "chiffré, il déplace l'indice de $0{,}002$ là où la convention $1/R$ le déplace de $0{,}036$ :\n",
    "**quinze fois moins**. La loi de rang tient, et ce qui la menace n'est pas la page — c'est de ne\n",
    "pas l'avoir mesurée.\n",
    "\n",
    "**Et l'ordre des priorités s'inverse.** Ce qu'un régulateur doit exiger d'abord n'est pas le\n",
    "format servi, mais la **mesure de l'exposition elle-même**. La colonne `format` de la\n",
    "[demande d'accès](../article-40.md) reste utile — elle permet de vérifier ce chapitre sur une\n",
    "autre plateforme — mais elle cesse d'être nécessaire au calcul de l'indice.\n",
    "\n",
    "**Réserves.** L'appariement à contenu fixé ne retient que $1\\,106$ strates sur $404\\,478$ : ce\n",
    "sont les documents servis au même rang dans des pages de compositions différentes, et rien ne\n",
    "garantit qu'ils ressemblent aux autres. L'appariement par requête, indépendant, concorde — mais\n",
    "il est sept fois moins peuplé et n'établit rien seul. Enfin, le regroupement de `media_type` en\n",
    "« ordinaire » contre « enrichi » reste un choix de ce dépôt."
   ]
  }
 ],
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