How actions and words come to make sense in a continuously changing world of work: A case study from software development
Bibliographic record
Abstract
Abstract To be successful, collaboration at work requires its participants to have a common sense about what is happening and where things are heading. But how can collaborators have such a sense in common if what is going on continuously changes? This study investigates the joint communicative work participants in collaborative activity do to remain aligned on how things are going and where things are at for the purpose of maintaining a ground in common. Our test case for illustrating this joint work is the fluid and constantly changing world of software development. Our study uses a transactional approach to show how software developers working together continuously make available what they are attuned to, which constitutes their common ground that allows actions and talk to make sense. The common ground enables a common, inherently shared sense of what is happening and how things are going. Rather than having “meaning” in themselves, signifiers (words, gestures, body movements, cursor movements) create and are part of the common ground against which they make sense. Signifiers are motivated by and produce an accented visible that is available to all group members; and this accented visible (including the signs) makes for the common ground.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".