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
Much of the knowledge management (KM) literature focuses on ways to increase the volume of knowledge available to employees, ensure its quality, and improve its accessibility. Such supply-side arguments are limited to the extent that they do not address the demand for knowledge within organizations. This paper takes a novel approach to understanding how access to others' knowledge produces benefits by studying the extent to which individuals intentionally access each other's expertise, experience, insights, and opinions, which we term knowledge sourcing. A general model of knowledge sourcing, including contextual and dispositional antecedents and learning outcomes, is proposed and validated using survey data from a global organization. Knowledge sourcing explains a significant proportion of individuals' learning outcomes, but the strength of this effect is moderated both by the strength of individuals' learning orientations and the degree to which they find their jobs to be intellectually demanding. For researchers, this study extends existing knowledge by proposing, testing, and validating a new way to understand an important KM issue in organizations. Practitioners can use these findings to evaluate existing KM efforts and better target future KM interventions towards those individuals most likely to benefit.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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