Representing School Success and Failure: Media Coverage of International Tests
Bibliographic record
Abstract
It is through the media that audiences come to learn about the apparent successes and failure of the education system. Despite this power, the connection of the media to educational leadership and policy making is often given little attention in determining the forces at play in evaluating what happens in schools. Using a critical discourse analysis of media coverage concerning the 1999 Trends in Mathematics and Science Study (TIMSS) and the 2000 and 2003 Programme for International Student Assessment (PISA), the author argues that the media interpreted these test results in concert with business and electoral elites as a ‘failure of marginalized students,’ rather than a failure of society to address systemic discrimination. The media coverage of such failures presents solutions provided by business and government as common sense. Consequently, alternative framings, for example, as to what a successful education system would look like to people who are judged school failures based on the tests are never sought. There is also no discussion of the ways in which the PISA and TIMSS tests are constructed to favor the knowledge of dominant interests and ignore that which is outside this realm.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".