Bibliographic record
Abstract
There are many reasons why a budding academic might want to avoid interdisciplinary research.It is diffi cult enough to acquire expertise in one fi eld of research, let alone two or more.The time required to read the literature in a fi eld outside your own main area can be hard to fi nd, and the additional time investment to learn novel methods from another fi eld can be huge.Moreover, the hiring and reward systems in academia still run strongly along disciplinary lines, so that work that draws on or contributes to other fi elds may not be fully valued in one's own fi eld.Interdisciplinary research may not be appreciated by narrow-minded colleagues.Some interdisciplinary projects have a bogus air about them, looking like they were designed more to bring in big research grants than to accomplish intellectual goals.The interdisciplinary scholar can look a bit like a dilettante, dabbling in multiple fi elds in order to avoid tackling the diffi cult problems in an established fi eld.Grants for interdisciplinary research can be diffi cult to get, because most granting agencies are organized along disciplinary lines.Despite these deterrents to interdisciplinary research, there are powerful intellectual reasons why work that oversteps the ossifi ed boundaries of established fi elds can have great intellectual benefi ts.Such benefi ts are vividly apparent in the interdisciplinary fi eld of cognitive science, which attempts to understand the mind by combining insights from the fi elds of psychology, philosophy, linguistics, neuroscience, anthropology, and artifi cial intelligence.After a brief review of the history of the fi eld and its contributing disciplines, this chapter will examine some of the main theoretical and experimental advances that cognitive science has accomplished over the past half century, deriving lessons that might be useful for researchers in any emerging interdisciplinary area.16.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".