SECONDARY SCHOOLS IN BRITISH COLUMBIA: A CRITICAL DISCOURSE ANALYSIS OF HOW THE MECHANICS OF SYMBOLIC CAPITAL MOBILIZATION SHAPES, MANAGES, AND AMPLIFIES VISIBILITY
Bibliographic record
Abstract
ii In the discourse on how to improve British Columbia’s secondary schools two prevailing epistemological tensions exist between two competing rationalities: (1) an instrumental rationality that privileges sense-making born out of data-gathering, and (2) a values-rationality that is discernibly more context-dependent. The seeds for public discord are sown when a particular kind of logic for capturing the complexity of any problematic is privileged over a competing (counter) logic attempting to do the same thing. The Fraser Institute proposes to the public a particular vision on how to improve secondary schools by manufacturing annual school report cards that are published in newspapers and online. Proponents of school report cards believe that school improvement is predicated on measurement, competition, market-driven reform initiatives, and choice. They support the strategies and techniques used by the Fraser Institute to demarcate the limits and boundaries of exemplary educational practice. Critics of school report cards object to the way ranking rubrics highlight and amplify differences that exist between schools. They
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.051 | 0.024 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".