Enhancing Scholarly Creativity When Developing Research Ideas
Bibliographic record
Abstract
This study details how 17 introductory psychology and sociology research methods textbooks suggest students develop research ideas. It compares this advice with how social psychologist Stanley Milgram creatively invented five research projects. Of the textbooks that offered advice on idea creation techniques (n = 13), most recommendations conflicted with Milgram’s most relied upon inventive approach. The textbooks promoted a range of inventive techniques, many of which encouraged students to undertake, in their initial area of interest, a review of the previous literature. Milgram’s favored inventive approach differed: often with students, in one sitting he envisioned, developed, and honed research ideas and then scoped out the potential study’s entire methodological design. At best, Milgram’s review of the previous literature came after having settled on a methodological approach. In doing so, he broke a common textbook golden rule: before deciding on the methodological design, the previous literature must be reviewed. The textbooks often warned against the inventive approach Milgram deployed, describing it as imprudent: the idea developed may already have been completed. This valid criticism, however, fails to consider a potential advantage associated with Milgram’s unconventional approach: his ignorance of the previous literature ensured his creative lens remained unadulterated by the powerful influence of what had been done before. It is concluded Milgram’s unconventional approach to idea creation may, at least in part, explain why he was so creative. This paper concludes with a compromise position: researchers should be exposed to the strengths and weaknesses associated with both the most common textbook approaches to idea generation and that of creative high-impact scholars like Milgram.
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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 | 0.122 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.031 | 0.022 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".