Learner agency in urban schools? A pragmatic transactional approach
Bibliographic record
Abstract
Enhancing learner agency in urban schools is seen as increasingly important in educational policy for narrowing existing attainment gaps. However, notions of learner agency are contested and require conceptual clarity. To help generate such clarity a conceptual synthesis of the field was undertaken that resulted in a mapping framework around three distinct and yet interrelated perspectives: (a) micro-individualistic self-authoring, (b) macro- and meso-level structural and cultural determination, and (c) macro-meso and micro level conflation and interconnection. Based on conceptual shortcomings in the field, we develop our own fourth approach that focuses on a relational, pragmatic transactional perspective. This suggests the young person as a separated learning agent is an incorrect unit of analysis to explain intent behind action. Instead we are argue for the holistic and integrated (Deweyan) notion of transaction that focuses on young people being subject and subjected to conditions as much as being subjects of their conditions.
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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".