The Sustainability of Corporate Wikis: A Time-Series Analysis of Activity Patterns
Bibliographic record
Abstract
While existing theoretical frameworks describe collective technology adoption patterns, they provide little insight regarding the expected patterns of wiki activity within projects. Another impediment to the study of wiki sustainability is the absence of time-series analysis methods that are suitable for the unique patterns of wiki activity logs. The primary goals of this study are to: (i) develop a novel method for analyzing wiki edit activity logs, (ii) reveal the temporal patterns of corporate wiki edit activity, and (iii) study the factors impacting wikis' sustainability. A validation of our proposed method demonstrates that it is superior to the baseline algorithm in the face of noisy data. Our empirical study combines wiki system edit activity logs with a survey of users' perceptions, and explores 33277 distinct wiki applications within one global organization over the first 5 years of wiki operation. Our results reveal six different prototypical wiki activity patterns, and show that most corporate wikis become inactive after a relatively short period. Findings from the user survey show that users of sustainable wikis are more satisfied with the wiki system and its contents, and feel that the wiki provides them with a sense of community and productivity enhancements
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.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".