Bibliographic record
Abstract
Abstract The influence of extralocally produced texts, such as professional standards and systems of accreditation, on the ruling relations that govern teachers’ work and their learning about that work is a matter of concern in Australia, as it is in Canada, UK, and USA. This chapter explains how a dialogic analysis and the construction of individual maps of social relations were employed to reveal the influences that governed teachers’ learning about their work at the frontline. A dialogic analysis of research conversations about learning, based on the work of Mikhail Bakhtin, revealed the existence of both centralizing, hegemonic discourses associated with a managerial agenda and contextualized, heterogeneous discourses supportive of transformative learning. It also revealed the uneven influence of extralocally produced governing texts on both the locally produced texts and the “doings” of individuals. The production and use of “individual” maps represents a variation on the way “mapping” has generally been used by institutional ethnographers. From these informant specific maps, we can begin to observe some broad patterns in relation to the coordination of people’s “doings” both within a given context and from one context to another.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".