Bibliographic record
Abstract
In this article, I discuss recent media attention paid to consciousness research through coverage of the “25 years of consciousness” event at the 2023 Association for the Scientific Study of Consciousness annual meeting, including:NatureScienceThe EconomistThe New York TimesScientific AmericanScienceAlertTheDebriefNewScientistNautilusI present critical additional context to these pieces, including how they should be considered in the broader field of consciousness studies, and how the field is perceived by funders and the general public. My two primary arguments are that the field is richer and more complex than some recent media portrays, and that skepticism about the rigor of the field – fueled in part by oversimplification in some media reports – places great pressure on trainees’ professional prospects. I feel it is critical to expand on current reporting with additional nuances that I can share from my own involvement in the field, in order to avoid public misperception of the results discussed at this event.
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 | 0.094 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".