Transdisciplinary approaches to critical data studies
Bibliographic record
Abstract
ABSTRACT In this paper we describe the rationale behind a panel concerning the transdisciplinary approaches to critical data studies. We illuminate emerging modes of conducting critical data studies research and highlights the interdisciplinary opportunities within future research directions. We describe the format for the panel and the themes that will be covered by each of the author. By having this panel to occur it creates a space for interdisciplinary dialogues on a timely topic that touches various sub‐ fields of information science along with the broader social science disciplines in the academy.
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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 | 0.279 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.019 | 0.088 |
| Scholarly communication | 0.029 | 0.022 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".