Bibliographic record
Abstract
I do not think it would be a surprise to most readers if I were to mention that the Action Research community is not a particularly unified one. In fact, I am of the belief that the very philosophy and methodology that typifies this movement prevents us from becoming a solid vanguard of like-minded researching replicas. The bricks and mortar of Action Research are just too porous for this to happen. Instead, we in Action Research seem to be more interested in using it to serve different purposes rather than concerning ourselves with being members of any specific gang. Some members of the Action Research movement examine a class they are teaching to find ways to make student learning more efficient, integrated or accessible. Others are more committed to engaging in larger Action Research projects where stakeholders work together to create a learning community.
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 | 0.197 | 0.349 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.015 | 0.117 |
| Scholarly communication | 0.031 | 0.031 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".