Bibliographic record
Abstract
Summary This paper presents a novel analysis about the capabilities of academic researchers and academic poverty. Adopting the capability approach, which Amartya Sen developed to address concerns such as poverty, inequality and development, we shift the focus of analysis and discussion around evaluating academic research and academics in the social sciences from measures of so‐called ‘quality’, ‘impact’ or ‘excellence’ to the capabilities of academics. For us, the conceptualization and evaluation of academic research is a question about what academics have reasons to value, and about their ability to achieve valuable beings and doings. It is also about determining what might constitute academic poverty, and what academics are required to do in order to avoid that poverty. Relating our analysis to debates around universities, in particular about quasi‐market pressures, we identify the possibility of basic capabilities in academic research, namely: the capabilities that are necessary to fulfill basic academic needs. Our proposition is that there is academic poverty when an academic researcher is not capable of fulfilling basic academic needs, such as: adhering to standards of coherence, robustness and rigour; searching for and disseminating the spirit of the truth. Moreover, if the academic has the capabilities to fulfill those basic academic needs and yet chooses not to do so, she renders herself in a state akin to academic poverty.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".