Adventures in Research Land: <i>Another Glance “Through the Looking Glass” to See What Constitutes Research</i>
Bibliographic record
Abstract
There are many frames and rules of thumb for determining what constitutes 'research'. Some views are directed by the perspective or philosophical underpinnings of particular disciplines or approaches to research. Others are guided by rules concerning or definitions of what 'evidence' is, or whether 'new' knowledge is created. A recent paper by Jarvis suggests that we should differentiate among research, evaluation and measures to assure quality, and that this may help us steer a course through the roles and reasons for these various and varying activities. While the desire to clarify some distinct territory for what constitutes research versus something else is understandable, we argue that these distinctions in the end are at best unhelpful, can be misleading and actually do more harm than good--which in itself is an outcome that good research should avoid. This brief report is not a critique of the specific nomenclature suggested by Jarvis or other existing frames for identifying 'research'. Our intent is rather to begin a more general commentary on the very subject raised near the end of Jarvis's paper: "The three primary approaches to reviewing what we do are research, evaluation and quality assurance. There are similarities, differences and overlaps among these three approaches. They are part of a continuum with no clear distinctions between them."
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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 | 0.074 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.085 |
| Scholarly communication | 0.035 | 0.050 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.015 | 0.037 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".