One perspective on <i>Conversation Analysis: Comparative Perspectives</i>
Bibliographic record
Abstract
Preamble The title of this book is Conversation Analysis: Comparative Perspectives , a title which appears to offer a well-defined promissory note about the nature of its contents. And so it does. There is an introduction which sets out some central characteristics of conversation-analytic (CA) work, and briefly reviews the history of comparative analysis in anthropology – together with some of the problems confronted in the course of that history. A number of the substantive chapters that follow report work that is comparative in its very nature; most of the authors of chapters in which this is not the case go out of their way to set their respective topics in comparative context – either by including data from other language/culture settings or by reviewing (some of) the literature which sets their work in a comparative framework. For a readership that is (I suspect) largely drawn from the so-called “social” or “human” sciences – anthropology, linguistics and applied linguistics, communication, psycholinguistics and cognitive science, social psychology, and sociology, this is what one would expect such a volume to provide … from its sub -title. But its main title should make relevant as well other dimensions of comparison than the linguistic and cultural ones, and, before settling down to address what is actually in this book, I would like to use my bully pulpit to call to mind other “comparative perspectives” that ought to figure importantly in CA work, or at least be taken into account, even when they do not figure centrally. Like what?
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".