Bibliographic record
Abstract
The Invisible Influences on Decision-Making explores the often unseen factors that influence decision-making in both everyday life and various professional settings, with a particular focus on policing, medicine, education, and counselling. Chitpin and Dougan present key findings and trends in decision-making research, then go on to explore the concept of indecisiveness before examining decision-making processes in these specific professions. The authors employ qualitative research methodologies, such as case studies and phenomenology, alongside social influence theories, to provide a nuanced and current understanding of decision-making within these fields. By incorporating perspectives from professionals, chapters aim to enhance public understanding and confidence in how decisions are made in these critical areas, especially within a Canadian context. While there is existing literature on decision-making in community service professions, it often remains discipline specific. The Invisible Influences on Decision-Making addresses the need for more inter-professional and interdisciplinary research, highlighting the collaborative nature of these professions and the complexities of their decision-making processes. By integrating real professional examples with research and theory, this study offers a compelling and insightful narrative that will engage researchers, practitioners, and graduate students across various fields.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".