From words to connections: Word use similarity as an honest signal conducive to employees’ digital communication
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
Bringing together considerations from three research trends (honest signals of collaboration, socio-semantic networks and homophily theory), we hypothesise that word use similarity and having similar social network positions are linked with the level of employees’ digital interaction. To verify our hypothesis, we analyse the communication of close to 1600 employees, interacting on the intranet communication forum of a large company. We study their social dynamics and the ‘honest signals’ that, in past research, proved to be conducive to employees’ engagement and collaboration. We find that word use similarity is the main driver of interaction, much more than other language characteristics or similarity in network position. Our results suggest carefully choosing the language according to the target audience and have practical implications for both company managers and online community administrators. Understanding how to better use language could, for example, support the development of knowledge sharing practices or internal communication campaigns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.019 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it