Bibliographic record
Abstract
If you are engaged in a serious conversation about Action Research with someone in the teaching profession, the chances are this person is either a pre-service candidate or an educator who has just emerged from some form of related in-servicing. Of course, this is a terribly broad and perhaps clichéd overstatement. Nevertheless, it cannot be denied that this methodology easily finds a home in these two areas of a teacher’s education. In all probability, teacher educators may introduce this practice as a way of counter-balancing people’s past experiences. Ever since the statement “apprenticeship of observation” was made by Lortie (1975), an abundance of research has shown that most students entering teacher education programs do so with a fairly ingrained conception of the role of teachers and the nature of the students they teach (Hollingsworth, 1989; McDiarmid, 1993). These beliefs may vary with people’s histories and circumstances, but there is now little doubt that, for good or ill, they are highly stable and complex, unchangeable even in the face of new experiences or strong outside arguments. Depressingly, Kenneth Zeichner’s work has further shown that most new information or techniques taught during traditional teacher education programs or in-servicing courses quickly “wash out” in the face of past experience (Zeichner & Tabachnik, 1981).
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 | 0.185 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.112 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.027 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 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".