Hindered Help: Barriers to Giving Social Support in Relationships and Organizations
Bibliographic record
Abstract
From advice to DEI advocacy, individuals and organizations thrive when they receive effective social support from others. Yet, giving effective support is often hindered by diverse psychological barriers. From whom do people seek support? What kinds of support do people give? And how? This symposium presents the latest research on the nature and consequences of barriers to social support provision. Our papers offer critical insights into how people think of their ability to give support in diverse contexts. Our papers also provide novel strategies to foster more inclusive relationships and work environments. Experiential Authority: Why People Seek Advice From Those With Direct Experience Author: Rachel Lise Ruttan; University of Toronto Author: Daniel J. Chiacchia; University of Toronto Author: George Newman; University of Toronto It’s Not My Business: Perceived Responsibility in Supporting Friends’ Goal Pursuits Author: Yena Kim; University of Chicago Author: Fan Yang; University of Chicago Author: Emma Levine; Advising Across Identity-Relevant Trade-Offs Author: Ibitayo Fadayomi; Author: Erika Kirgios; Author: Emma Levine; It’s Easy to Learn, Save Money, and Workout: When Framing Tasks As Easy Can Backfire Author: Samuel Skowronek; University of California Los Angeles Author: Rebecca Schaumberg; University of Pennsylvania Diversity Error Risk: When Perceptions of Risk Deter Versus Enhance DEI Advocacy Author: Rachel D Arnett; Wharton School Author: Jared Scruggs; Author: Katherine Chen;
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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".