Implicit informal qualitative research processes embedded in legal proceedings: a case example.
Bibliographic record
Abstract
OBJECTIVE: To make manifest, through a qualitative research process, the competing meanings constructed by the various parties in a legal case based on their own phenomenological perspective and/or experience of the events that gave rise to the legal complaint. METHODS: Representative quotes from the documentary court filings of various parties in a U.S. Supreme Court case, Safford Unified School District v. Redding, involving a highly emotional issue-strip search of a child at school by school officials -provide the data source. These texts are analyzed conceptually to discover themes which help summarize the parties' diverse phenomenological perspectives on the 'facts'. RESULTS: The sample texts from the court filings in the case were readily organized by themes and the competing parties' conflicting perspectives located on opposite ends of various continuums described in terms of those themes. CONCLUSIONS: Making sense of conflicting legal positions can be considered, in part, as an informal qualitative research process. The use of textual analysis, a qualitative research process, can greatly assist in making more explicit the conflicting phenomenological perspectives of the various parties latent in the hundreds of documents typically filed with the courts in any major case. This may be helpful in mediation.
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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 | 0.062 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| 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".