Bibliographic record
Abstract
For my doctoral research into adults’ informal learning through material objects in four public places in Halifax, Nova Scotia, I used sketchbooks as fieldnote journals. In contrast to objective observations, I recorded during my site visits a panoply of overheard conversations, drawings, remarks, puns, encounters, temperatures, and colours. These and other elements comprised my experiences in each site, and I wanted to represent their gist and connotations through multiple forms of expression. This approach aligns with arts-informed research methodology that celebrates complexity and shared meaning-making with engaged scholarship. I used these notes to produce for each site a written vignette, to introduce and reacquaint others with that place; two of these vignettes appear in the following report. In translating what I came to call my “feel’d,” not “field,” notes into these written pieces, I gleaned new understandings about scribbling and scrawling expressive, affective feel’d notes. I found that engagement enriched my research process, and also fostered a greater awareness of place meanings. I recognize that transformed notetaking has a bearing on understanding, research process, people/communities, and places, and offers methodological insights that carry out and further engaged scholarship knowledge.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".