Ethnostatistics and Organizational Research Methodologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ethnostatistics is the empirical study of how professional scholars construct and use statistics and numerals in scholarly research. This article provides an overview of the objectives, contents, and contributions of the current theme issue. The nature and relevance of ethnostatistics to organizational issues are discussed. Three levels of ethnostatistics are identified and explained— constructing statistics, statistics at work, and the rhetoric of statistics. The contributions the theme issue provides to these three levels of ethnostatistics are discussed. Foundational perspectives that have shaped ethnostatistics are explored to highlight important assumptions of the field and to distinguish ethnostatistics from related fields. The theme issue broadens the field of ethnostatistics to address statistical practices used by business professionals for organizational purposes. The article concludes by arguing that the field of ethnostatistics needs to develop rapidly at this point in time to address the emerging centrality and importance that statistics hold for everyday organizational life.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it