Mixed Communities Require Mixed Theories: Using Mills to Broaden Goffman's Exploration of Identity within the GBLT Communities
Bibliographic record
Abstract
The central objective of this paper is to attempt to counter an overly-rigid theoretical approach in data analysis. Implicit in the push to identify and follow one proper theoretical stream is the idea that one's particular theoretical approach will always be plausible and contains an inherent ‘value’ over any other approach. That being said, the purpose of this paper is two-fold. The first is to argue that a rigid theoretical approach to understanding people from non-homogenized communities leaves the analysis wanting. Instead, I refer to a more flexible nature of using a mixed-method approach to analysis, which will generate an appropriately pluralistic representation of someone from a pluralist community. Secondly, this paper suggests that a mixed-method approach should include both a micro and a macro analysis. In this vein, I put forward the benefits of combining the theoretical approaches of both Goffman and Mills. In doing so, I am not suggesting that Goffman and Mills are the only theorists to use. Rather, the combination of these two theories is useful for understanding an intersubjective approach to myself. A flexible epistemological approach would recognize that other situations might call for the use of other theorists.
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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 | 0.072 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".