Bibliographic record
Abstract
There was a time, and not too long ago, that when you wanted to discuss your Action Research project, you usually found yourself talking into a mirror for company. This was due to the severe constrictions by the research community on scale, scope and interest for something that had little generalizability or transnational relevance. In short, if you were seeking answers to a question in your board, school, or classroom, it was of limited appeal to just about everyone not intimately related to the project. Connections could simply not be made. And if those who engaged in Action Research had waited around for government initiatives or high academia to lead the charge, we all still may be presenting only to our shadows. It is to our great benefit, therefore, that in the last few decades, strides have been made at the grassroots level to provide forums for discussion, debate, support and revenues for Action Research. Through these endeavours an authentic web of connections has been created between individuals, communities, and even countries. And in doing so, small scale Action Researchers may now find their place in the larger network: Where what they have discovered and what they have to say are valued as much as any other members of this loosely-coupled community. This is largely due to the very porous nature of the relations between the individual, the locality, the country and the global situation.
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 | 0.009 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".