Using Grounded Theory to Understand a Cutting-Edge Issue: Effects of Integrative Tactics on Chinese Gay Men’s and Lesbians’ Social Well-Being
Bibliographic record
Abstract
This study aims to demonstrate how grounded theory can be used to explore and analyze negotiation processes between self-identified gay men and lesbians and their parents. For a majority of Chinese gay men and lesbians, marriage proves to be the primary concern that drives negotiations with parents. Extant research documents the precarious consequences of gay men’s and lesbians’ social well-being yielded by these negotiations, which primarily employ distributive negotiating tactics. As integrative tactics prove to be conducive to favorable outcomes, their application in same-sex children’s negotiation with parents informs the present study. Semistructured interviews were conducted with 25 Chinese participants (15 gay men and 10 lesbian women). Grounded theory analysis of interviewee data identified a grounded theory of soft-power-based negotiation, which illustrated detailed negotiation processes between gay men and lesbians and their parents and critical conditions mediating this process. The grounded theory elaborated concrete soft-power bases and integrative tactics used by participants and their parents. Conditions for integrative tactics to sustain gay men and lesbians’ social well-being emerged. Results implied viable solutions for resolving conflicts between social minorities and social majorities in general.
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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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".