Bibliographic record
Abstract
How do we connect the personal and universal and how is intentionality connected to its union? The dilemma of connecting the personal and the universal has been one grappled by artists, poets, and performers for ages, but now questions come to us as ABER scholars, artists and educators to inquire where these connections are located. My philosophical underpinning has sought ways to connect the personal and universal, private and public and my scholarly, performance and poetic work has been rooted in the tradition of the lived curriculum and the soil of autobiography. This soil has always had the ingredient that our stories have the capacity to utter one another, and form and inform one another. My expression is not only for my own well-being, but a way I can both listen and offer a gift, an entrance point for someone else, to access their own understanding, perception, and perhaps be moved in some way (Snowber, 2005, Richmond & Snowber, 2009). This is the paradox, beauty and mystery of interconnecting autobiographical work, whether that is poetic, narrative, artistic or embodied ways of inquiry for others and ourselves.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.148 |
| Scholarly communication | 0.027 | 0.057 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".