Bibliographic record
Abstract
How shall we guide ourselves to ensure that research on creation and reception are ecologically valid, representative of natural processes in everyday life? Rather than romanticizing science, we need to be critically aware of the hidden assumptions which lie behind the empirical narratives that appear in journals. Francis Bacon awakened us to the “false idols” and Goethe encouraged us to place the careful observation of phenomena of the natural world ahead of theories which should be derived from observation and help focus our understanding of them. Following Bacon and Goethe, I advise that researchers should work from actual instances or episodes in which the phenomena of interest are manifested. Our goal should be to ensure that the parallel world of experimentation is not divorced from everyday life. This can be achieved by developing a large sample space of materials (poems, literary texts, films, and so forth) from which at least two must be chosen to represent each kind of stimulus. Further, we need to specify the orienting task set according to which participants are to approach these materials. Finally, I advocate the use of quantitative and qualitative kinds of data in a complementary manner to elucidate the underlying processes. By engaging in an empirical dialogue with ourselves and others, we can advance our understanding and explanation of the phenomena which fascinate us.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".