Educational Outcomes, Modern and Postmodern Interpretations: Response to Smyth and Dow
Bibliographic record
Abstract
Drawing on research in Canada as well as studies in Australia and elsewhere, this paper challenges the arguments of Smyth and Dow (British Journal of Sociology in Education, 19(3), pp. 291-303) that outcomes-based education necessarily embodies a form of technical rationality which routinizes, proletarianizes and deskills teachers and their work. This paper criticizes Smyth and Dow for applying a universalistic and monolithic labour process theory to outcomes issues, for paying little or no attention to published evidence and argument on outcomes, and for being insensitive to the variable geometries of the outcomes phenomenon across different contexts. This paper, by contrast, identifies how investigating outcomes in a context of socialist policy-making, where equity goals are explicit and high discretion is granted to teachers, can contribute to clearer understanding of the variable postmodern geometries and educational possibilities of outcomes technology. These possibilities include fostering stronger collegiality among teachers, and democratic inclusion of pupils and parents in the teaching and learning process.
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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 | 0.045 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.085 |
| Scholarly communication | 0.019 | 0.039 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.037 | 0.057 |
| Insufficient payload (model declined to judge) | 0.004 | 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".