Bibliographic record
Abstract
Insider research poses a range of benefits and challenges for researchers and the communities being researched. It is commonly advocated for disability research but there is limited work exploring disabled researchers’ experiences. Influenced by autoethnography and through a process of asynchronous structured conversations, we reflected on our experiences as two blind researchers. Through our collective reflective process and analysis, we created three main themes: insider research is complex and subjective, there is judgment about the “right” thing to do, and insider research requires “different” work. We argue that insiderness is more than sharing characteristics: it is a situated, fluctuating, and “felt” experience. The complexities, judgments, and emotional labor associated with insider research can challenge researchers in potentially very personal and unexpected ways. We propose that further investigation is required about how researchers can best prepare for, engage ethically throughout, and be supported through the insider research process.
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 | 0.123 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.038 | 0.107 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".