Notice bibliographique
Résumé
Our current society is seeing the impact of compounding vicious cycles of disparity, dividing our society between those that are well resourced and those that are not.This expanding disparity is not limited to wealth, but is also at play in education, employment, research (or being understood), digital access and influence.Although the democratizing potential of emerging socio-technical practices such as global networks could disrupt these cycles; the design of technologies such as search engine optimization, social media promotion, and Big Data analytics only amplify and speed the widening of the gap by creating echo-chambers of popularity and attention.This means that those with advantage and privilege gain more wealth and influence creating an ever-widening gap.This has dire consequences for society as a whole. 1 One locus of intervention that has the greatest promise to address this critical dilemma is education.Investment in inclusive education and education about inclusion has a multiplying effect that can garner an impact that far exceeds the initial effort.However, to achieve inclusive education we must address a number of entangled factors.Chief among these are a) our systems of research, inquiry and evidence depended upon to expand knowledge and advance quality, and b) inclusive access to digital systems that have become integral tools of education. Current Research Methods, Diversity and ComplexityLike our markets, our systems of research and evidence are systemically biased against diversity. 2In our attempt to understand complexity and find dominant patterns, we elide the outliers. 3This creates compounding disparities that ripple well beyond the topics of research.Our current systems of academic research leave a host of issues and individuals stranded at the edges: students who don't fit under the constraining mantle of average or the clusters of recognized classifications, patients whose unique condition means there is not a large enough representative research sample to reach statistical power to draw generalizable conclusions, or consumers whose unique needs will not warrant a product because the size of the customer base will not be profitable.Persons that do not fit into any representative sample are less likely to be represented by research or scholarship and are less understood, or worse, they are misunderstood and misrepresented.This has implications for policy, markets, systems of education, systems of employment, government services and all facets of life.Demographics show that these margins may collectively outnumber the "norm". 4 5,6However, our current systems of research funding perpetuate this pattern -leaving peerless research, research that cannot achieve statistical significance, subject matter without well-established disciplinary backing, and academic institutions that do not already pass high impact metrics without the needed support to sustain inquiry.This dominant and pervasive pattern of formal research puts our society at risk of knowledge blind spots: hampered in predicting the occurrence of threats; unable to
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,019 |
| Communication savante | 0,014 | 0,026 |
| Science ouverte | 0,002 | 0,028 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».