Symbolic Interactionist Ethnography: Toward Congruence and Trustworthiness
Bibliographic record
Abstract
In social science literature, researchers have discussed the implications of using symbolic interactionist ethnography. While the value of such an approach is accepted in social science research, it has not received adequate attention in IS literature. More importantly, there is a need to begin developing consensus on a set of principles to help researchers effectively combine the theoretical strengths of symbolic interactionism with the empirical strengths of ethnography. In this paper, we take a step forward in that direction by firstly highlighting the importance of the interactionist ethnography approach to IS research. We then describe how the value of such an approach can be realized through achieving congruence and trustworthiness in the research process. Finally, an IS research proposal is used as a practical illustration of how this congruence and trustworthiness can be achieved. By thus combining the strengths of symbolic interactionism and of ethnography, the interactionist ethnography
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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 | 0.143 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| 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".