Bibliographic record
Abstract
feedback dangers 202-3 literature reviews 100, 102, 109-10 referencing systems 200 use of paraphrasing 198 action research 49-50, 67, 235 activist methodology 48, 235 Android 228 anecdotes 71-2 ANOVA (analysis of variance) 176-7 APIs (application programming interfaces) 128-9, 235 appendices 196 Apple iCloud 229 Apple iWork for iCloud 229 Apple Mac 227, 228 appendices 196 apps 54, 228-30 time management 189 use of cloud-based services 7-8, 229-30 archival data 126-7, 235 arts-based research 52-3, 235 audio software 160-1 audits, for time management 83-4 Australia 6 SRoI 27 survey data 131 averages see mean/median/mode B background research 99-118, 235 backing up work 7-8, 229-30 Bad science (Goldacre 2009) 108 Barber, R. et al. 22-3 benchmarking between services, use of SRoI 29 bibliography 104-5, 235 BioMed Central 110 bivariate statistics 174-5, 235 Bourgois, P. 27 Index British Library 114-15 C Canada data centres 124-5 library services 115 survey data 130 case study research 152-3, 236 CAT software 168 census data 129 CESSDA (EU) data archives 125 Chandler, Raymond 189 chi-square test 174-5, 176 children 87, 88-9 consent issues 151 use of 'draw and write' data 151 'chunking' workload 79, 82 writing 188-9, 191-2 citations and references 200-1, 236, 243 how many 113-14 record-keeping 103-5, 226 closed questions 137, 139, 236 cloud-based services 7-8, 229-30 drawbacks 229-30 cluster analysis 176-7 codes of conduct 29, 233-4 coding see data coding 'coding frames' 165-7, 168, 236 collaborative projects 21-2 commissioning research 12-13 communication skills 4 doing interviews 141-2 computer operating systems 227-8 compatibility and collaboration 228-31 computer software applications/programs 228 costs of 230 formatting of files 228-31 free and open source (FOSS) 171, 182, 230 see also web-based research publishing consent see informed consent constructionist methodologies 45-6, 48, 236 content analysis 68, 178, 236 content validity see face validity continuing professional development 86-7 convenience sampling 70, 236 correlation coefficients 174-5, 176, 236 Cottrell, S. 107 counting methods 135 covariate relationship 236 creative data collection methods 153-4 creative research dissemination 214,
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".