The power of negative thinking in and for teacher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an anxious world increasingly perceived in terms of risk management, strategies for mapping, articulating and organizing knowledge provide a bulwark against uncertainty. For teacher education, one consequence has been a drive for fullness in relation to knowledge about what teachers should know and be able to do, usually conceived in instrumental terms. Indeed, teacher education, like education more broadly, has been captured by a ‘discursive duopoly’ of instrumentalism, involving the pervasive view that the main purpose of education is to serve the needs of the economy, and consensualism, involving the valorization of agreement regarding this purpose. One way to understand this dynamic in teacher education is in terms of positivity and negativity, whereby the positive refers to social structures, institutions and policies – best practice or teaching standards – that have become reified, while the negative is that which unsettles and disrupts the comfortable stance of the given order of things. Negativity, as a political practice that engages with the positive to reveal the historical and contingent nature of all knowledge, thus offers new conceptual resources, such as antagonism, dissensus, fantasy and impotentiality, for imagining alternative scenarios for teacher education beyond the confines of current policies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it