Are full and partial knowledge sharing the same?
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
Purpose This paper to examine full knowledge sharing (KS) and partial KS in order to test the proposition that they are separate behaviors with different characteristics, risks, and motivations for the informer and subsequently different predictors. Design/methodology/approach Employed knowledge workers completed two questionnaires over a two‐week period regarding their attitudes, situational factors, individual differences, and KS behaviors with their close colleagues in their workplace. Findings Results support the proposition that they are different albeit related behaviors. Full KS is enabled by intentions for full KS. Partial KS is enabled by the uniqueness of the knowledge, interpersonal distrust of close colleagues, and inhibited by perceived value of knowledge. Management support, interpersonal trust and distrust enable intentions for both full and partial KS, then propensity to share further enables full KS, and psychological ownership further enables intentions for partial KS. Research limitations/implications The findings from the study suggest that researchers should specify which sharing behavior they are examining (full or partial). Future research should also examine the outcomes of these two behaviors to see whether the assumed benefits of sharing knowledge apply to both of them. Practical implications The findings of the study provide some insight for practitioners on what motivates full versus partial KS. Originality/value The study challenges the assumption that KS is a single behavior, and starts to parse out the complexities within the KS literature with respect to predictors of actual KS behaviors.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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