Review of: "Bridging Empirical Evidence and Diverse Epistemologies in Public Policy: Evidence-Based Policy and the Issue of Subjugated Knowledges"
Bibliographic record
Abstract
Potential competing interests: No potential competing interests to declare.This article is both extremely interesting and highly promising.The numerous references demonstrate a highly relevant interdisciplinary approach to an original and important topic.Several central points are particularly noteworthy: the pseudo-rationality of political actions, evidence-based policy (EBP), the technocratic nature of knowledge, the issue of subjugated and marginalized knowledge, intersectionality, and radical democracy.The proposal of a framework for better understanding how to integrate marginalized or subjugated knowledge into EBP is especially compelling.However, the text sometimes lacks rigor in the articulation of concepts and coherence in the sequencing of ideas.The argumentation could be more focused, and the definitions of the key terms need to be clarified consistently.At times, the sentences are too long, with multiple complex concepts, some of which are used in an approximate way or require further elaboration (e.g., technocratic, true, empirical, etc.).Key concepts and their links should be identified in order to establish a clear problem and answer it through the various sections.The end diagram is an excellent idea, but it would benefit from a clearer problem statement and definition of key concepts.I hope my comments will help the authors improve this important work.
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 | 0.033 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".