13. Speculative futures for higher education: weaving perspectives for good
Bibliographic record
Abstract
Using a speculative future use case, and a three-part multiple author format, this chapter creates a container for a conversation amongst co-authors representing various roles including online students, faculty, and administrators, regarding a change in teaching and learning. In doing so, the chapter attempts to cross theory-practice-policy lines to provide a contextualized, systemic examination of a possible iteration of higher education. The aim of this effort is to grapple with the question of ‘goodness’ given a specific context and situation, rather than with the question of ‘goodness’ in universal terms. Through the response from co-authors, and the analysis and synthesis that follows, this chapter aims to problematize the universality of what it means for futures to be “good,” highlight the messiness of speculative futures, and make visible the ways in which roles, values, identities, ideologies, and systems shape the ways in which learning futures are perceived to be “good.”
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".