Bibliographic record
Abstract
How can I write about liking the UK Research Excellence Framework’s (REF’s) new emphasis on ‘impact’ (see Rogers et al., this issue), and looking forward to its increased importance in the next REF? The REF’s new ‘impact’ agenda opens up considerable potential for less traditional research to be officially valued. I agree with Rachel Pain et al. (2010, 185) that it “presents radical scholars with new opportunities to exceed the apparent limits of the audit game, in ways that allow geographical research to contribute to wider struggles to social change”. ‘Follow the things’ fits this description. It’s a radical research and public pedagogy project. It taps into public curiosity about ‘where stuff comes from’, and draws into its processes a proliferating genre of non-academic ‘follow the things’ work, including documentary films, art work, journalism and activism. Its intellectual / political purpose is to critique the fetishism of commodities, to show abstract relations between things as social relations between people (Harvey, 2010). Its pedagogical/political purpose, within and beyond academia, is to encourage critical thought, conversation and ‘do it yourself’ research that enables diverse people to follow their own things, consider the social relations and trade (in)justices in the lives of those things, and then to share, discuss and perhaps be activist with their findings (Cook et al., 2007a; Cook and Woodyer, 2012).
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.031 | 0.024 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".