Bibliographic record
Abstract
The PKP Scholarly Publishing Conference 2015, “Building Open Infrastructure and Programs for Digital Humanities, Publishing, and Libraries”, took place at SFU Harbour Centre, Vancouver, British Columbia, Canada, between August 11, 2015 and August 14, 2015. \n The official hashtag of the conference was #pkp5. \n This is a .zip folder containing three files. One is a .CSV file containing 1333 Tweets (∼1266 unique Tweets) tagged with #pkp5 (case not sensitive) from 212 unique users. These Tweets were publicly published and tagged with #pkp5 between 04/08/2015 10:11:01 and 18/08/2015 07:39:23 Pacific Time Zone. \n The zipped folder also contains a Summary CSV file and a ReadMe text file providing contextual information including methodology and limitations. \n The sharing of this dataset complies with Twitter's Developer Rules of the Road. Please read the ReadMe text file before using the data. Data might require refining and deduplication. \n If you use or refer to this data in any way please cite and link back using the citation information displayed above. \n \n \n
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.368 | 0.440 |
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".