Bibliographic record
Abstract
Exploring his academic life autobiographically, Joseph M. Shosh lays out his stance as a wrighter , or constructer of knowledge created largely through third-space dialectics. Experiencing firsthand both traditional and progressive methods of elementary education, he was placed within an academic track of a working-class secondary school before heading off to a small private liberal arts college where being academic required far more than memorization and formulaic recall. Consciously moving along a transmission to transactional continuum as a public-school teacher while learning to conduct practitioner inquiry as a doctoral candidate and early career professor led to inquiry-based curriculum development as Director of an action-research based master&s;s degree program for practicing schoolteachers. In an attempt to share the knowledge they were creating with a wider audience, Shosh and colleagues from Mexico, Canada, and the United States initiated an Action Research Network of the Americas (ARNA), democratically expanding knowledge construction and mobilization efforts by those not sanctioned by a traditional knowledge production industry. For Shosh, being academic has meant embracing dialogic, distributed learning with teacher-student colleagues and making a commitment for all human beings to engage in meaningful inquiry to invent and re-invent their worlds.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".