Qualitative Methodology, the Historical Sociologist and Oral Societies: Re-assessing the Reliability of Remembered "Facts
Bibliographic record
Abstract
With qualitative methodology now taking center stage in social sciences research, historical information in oral societies that pervasively relies on remembered categories could be sometimes fragmentary, biased, and willfully mis-located to effect a preferred relationship that may disturb or sustain currently desired power relations among groups of people. This paper will attempt to examine specific problems and challenges that pertain to the role of the historical sociologist who must not only record and interpret recalled events, but must also beware of possible "conflicts of interest" in the informant's/expert's relationship with the rest of society. The paper will use select examples from Somalia (East Africa) to show some possibilities of how and why people could manipulate historical data which, when published or reported officially, may facilitate their claim on resources and/or other preferred economic and socio-political outcomes. The paper proposes several ways to strengthen the situational reliability of the information received. URN: urn:nbn:de:0114-fqs0103219
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 | 0.302 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".