Bibliographic record
Abstract
Now a political sociologist working on social policy from a comparative and historical perspective, I have loved social theory since the first year of my undergraduate studies. And I must recognize that reading Bakhtin at an early stage of my academic development incited me to think of my own intellectual journey in dialogical terms. This may sound pretentious, but the phenomenon is as simple as it is universal: academics tend to frame their ideas as a reaction against ideas framed by other academics. Indeed, young scholars often imagine a dialogue between themselves and established authors who occupy a preeminent position in their field. Considering this, it seems appropriate to mention authors that inspired me while stimulating my critical thinking, forcing me to move forward. In fact, the most interesting authors are perhaps those who formulate provocative ideas that you feel you must criticize because they appear as both fascinating and inherently problematic. But in order to criticize a theory, to frame an alternative model, one generally draws on empirical findings and on alternative theories found in the literature. The dialogue then becomes subtler: a rich polyphony gradually replaces the original dialogue. This is especially true when the author is drawing on different intellectual traditions to formulate a new approach or, perhaps more modestly, an “original synthesis.”
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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.022 | 0.056 |
| Scholarly communication | 0.028 | 0.046 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".