Bibliographic record
Abstract
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.2 In our attempt to understand complexity and find dominant patterns, we elide the outliers.3 This 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 5,6 However, 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".