Becoming Scientific: Objectivity, Identity, and Relevance as Experienced by Graduate Students in Psychology
Bibliographic record
Abstract
The adoption of a rigorous experimentalism in the discipline of psychology has imposed tight constraints on what can be asked in psychological research and what sorts of answers given. Over the course of psychology's history the interpretive agent has receded into the background to make way for a more concrete observation language and a mechanistic, functionalist description of mind and behavior. In this context of disciplinary loss and gain, how do psychology's fledgling practitioners—its graduate students—understand the significance of their own research efforts? In this paper, we present thematic and discursive analyses of interviews with a sample of psychology graduate students at a large, public, research university in North America. We explore the manner in which the imperatives of "objectivity," as applied to psychological research, serve paradoxically to enhance the validity of what students feel their research permits them to claim while reducing its personal and social significance. We look at how, in this compromise, students struggle to define their identities as scientists so as to allay doubts about the significance of their work. Their comments provide insight into how psychological knowledge is critically evaluated inside and outside the discipline, and how these two perspectives are dialectically related. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1102260
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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 | 0.057 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.085 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".