Bibliographic record
Abstract
The notions of literacy and citizenship have become technologised through the demands for measurable learning outcomes and the reduction of these aspects of education to sets of skills and competencies. Technologisation is understood here as the systematisation of an art, rather than as intending to understand technology itself in negative terms or to comment on the way technology is used in teaching and learning for literacy and citizenship. Technologisation is approached here in terms of the understanding of literacy and citizenship as things (qualities, sets of skills) that one has. Drawing on the phenomenology of Gabriel Marcel the understanding of literacy and citizenship in terms of having is problematised, as is the distinction between having and being. This opens the way for a richer understanding of being literate and being a citizen explored through the figures of the Hermit and the Poet in Thoreau's Walden. Being literate and being a citizen are brought together here in order to consider the implications of their technologisation for academic writing in the university. The question of what we write in the name of in the university is considered in the light of this and of a particular notion of the public.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".