Sound, Presence, and Power: "Student Voice " in
Bibliographic record
Abstract
Every way of thinking is both premised on and generative of a way of naming that reflects particular underlying convictions. Over the last fifteen years, a way of thinking has re-emerged that strives to reposition school students in educational research and reform.i Best documented in Australia, Canada, England, and the United States, this way of thinking is premised on the following convictions: that young people have unique perspectives on learning, teaching, and schooling; that their insights warrant not only the attention but also the responses of adults; and that they should be afforded opportunities to actively shape their education.ii As will become apparent as this discussion unfolds, one of the challenges of analyzing this re-emergent way of thinking is that words and phrases such as “attention, ” “response, ” and “actively shape ” mean different things to different people. And yet a single term has emerged to signal a range of efforts that strive to redefine the role of students in educational research and reform: “student voice.” “Student voice ” has accumulated what Hill (2003) describes as “a new vocabulary—a set of terms that are necessary to encode the meaning of our collective project. ” These terms strive to name the values that underlie “student voice ” as well as the approaches signaled by the term.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".