Bibliographic record
Abstract
Jared Diamond asked the acclaimed evolutionary biologist Ernst Mayr (1904-2005) why Aristotle didn’t come up with the theory of evolution. Mayr’s answer was ‘Frage stellen’ which Diamond translates as ‘a way of asking questions [sic]’ (Byrne 2013). The idea that a particular way-of-asking might generate a particular way-of-knowing and, indeed, a particular branch-of-knowledge, is utterly intriguing, especially when we frame the practice of creative writing in those terms: as a way of asking questions. Drusilla Modjeska unpacks the concept of ‘temporising’ in her article ‘Writing Poppy’ (Modjeska 2002: 75). This discussion invites us to consider the generative capabilities of the temporising space – as an imaginative space for writers, as an alternate way of asking questions … of seeing, being, knowing. In narrative, the questions that underpin the work do not necessarily appear in the surface-content of the text. In this way, the story is a metaphorical representation of the questions that lie beneath. As Aristotle suggests, metaphor relies on ‘an intuitive perception of the similarity [to homoion theorein] in dissimilars’ (Ricoeur 1977: 23). In narrative we contemplate a question, or an idea, within the context of a metaphorical other. This is a form of temporising: of ‘slip[ping] into other time frames’ as a means of ‘retreat[ing] and consider[ing]’ (Modjeska 2002: 75, 76). In narrative time, we consider one thing through an alternate temporal lens. We prevaricate in otherness. Fiction-making represents a very particular way of asking questions. With reference to the process of writing the short story – ‘Everything that matters is silvery white’ – it is clear that ‘making’ narrative is a way of asking questions that is assisted by the transformative temporising space.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.046 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".