Bibliographic record
Abstract
Within the context of the UK’s Research Excellence Framework (REF), academic labor is being tagged to ‘impact’: to demonstrable outputs that go beyond academia and benefit “the wider economy and society” (HEFCE, 2009, 13; see also Rogers et al., this issue). This move is certainly not new, nor is it unique to institutions of higher education in the UK. ‘Impact statements’ have been standard in funding proposals for quite a while, grant funded projects have long required evidence of application within the communities where research occurs and, in the US, ‘service’ to institutional, professional, and broader communities is well established as one of the metrics used in governing promotion and tenure processes. In this intervention, we reflect on our experience working on an Economic and Social Research Council (ESRC) funded project where questions of impact – understood as efforts to engage participants and to produce applied results – were an ongoing concern. We offer a vision that recognizes that producing impact in research is a complicated process where alternatives to what some describe as the “wholesale neoliberalization of knowledge production” (Jazeel, 2010, np) might potentially be realized. More specifically, we offer an allegorical rendering of impact as odyssey.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".