The Australian Nons-Profit Sector, Knowledge Management and Social Media
Bibliographic record
Abstract
This poster presents key emerging findings from theonline survey which was conducted with a largenumber of Australian non-profit sector units to getinsight into the use of knowledge management (KM)practices as well as the use of social media such asFacebook, YouTube and Twitter, particularly in KMpractices. The findings from the data collected duringthe first month of opening of survey will be presentedin the poster. These findings will provide us withbetter understanding KM, NPO and social medialandscape which will have implications and learningfor NPOs operating in Canada and other countries.Cette affiche présente les principaux résultats issusde l’enquête en ligne qui a été menée auprès d’ungrand nombre d’unités australiennes du secteur à butnon lucratif afin d’obtenir une perspective surl’utilisation et les pratiques en gestion desconnaissances (GC) ainsi que l’utilisation des médiassociaux tels que Facebook, YouTube et Twitter, enparticulier dans les pratiques de gestion desconnaissances. Les résultats des données recueilliespendant le premier mois de l’ouverture de l’enquêteseront présentés sur cette affiche. Ces résultats nousfourniront une meilleure compréhension du paysagede la GC, des OSBL et des médias sociaux, ce quiaura des répercussions et un apprentissage pour lesOSBL en exploitation au Canada et dans d’autrespays.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".