Bibliographic record
Abstract
What is quality? Traditionally, quality was seen as an expression of the superiority of a product, meaning that it might work better, or last longer, or just look nicer. Usually higher grade materials were used and more care was put into its manufacture and finish. Usually a quality product cost more money, but was worth it if one took into account its attributes – and, of course, if one could afford the extra cost. But quality is not the same as luxury, which represents opulence, i.e. products that are better than they need to be to serve their primary purpose, and is typically more expensive. After all, even if you are a successful sales rep with a large territory you don't need a Lexus for driving around the country – but you do need a car that doesn't break down, is preferably not too fuel hungry, and is comfortable (because you'll spend several hours each day sitting in it). From a basic product manufacturing perspective, quality can be defined as conformance to specifications – specifications that are set by the manufacturer, based on the manufacturer's experience of what the customer wants. This can, of course, be discovered by carrying out customer surveys or by looking at sales figures. But in service industries the concept of quality is rather more difficult to define.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".