Bibliographic record
Abstract
absent parties' 6 ADESF litigation see under Brazil administrative schemes, public and private 4, 5 'compensation matrix', adopting 5 adverse cost risks see costs/adverse cost risks agency issues in class/group litigation 27, 36-40, 167 agency problems 39-40, 131-2, 145-6 adverse implications from divergence of interests 145-6, 212 attorney-initiated class actions, mitigation mechanisms absent in 146 client monitoring, mitigation of agency problems by 146 early settlement 146, 212 inherent nature of 393-7 insufficient investment in litigating 146, 212 individuals in cases, importance of 39 standing, models of ad hoc approved or certified non-profit entities 38-40 entrepreneurial lawyer 37, 39 governmental agencies 37, 38, 39-40 long-standing non-profit associations/designated entities 32, 37-8, 39-40 see also Reichart Industries/Shemesh litigation under Israel alternatives to class actions 4-5 ad hoc management strategies 4-5 administrative schemes, public and private 4, 5 distinguishing from class actions 5, 6 group litigation procedures 4, 5-6 informal ad hoc strategies 6 Aristocrat litigation see under Australia Atlas litigation see under Canada Australia 13-14Aristocrat litigation 139-40, 190, 198-202 background 198-9 'closed' class, restricting to 162, 200-202 commercial litigation funder 139, 191, 199-200, 202-3 control of the litigation 204 facts of 140 interlocutory litigation 200 lead plaintiff, appointment of 200, 202 opt-out process 200-201 settlement after trial 139, 190, 199, 202, 206 Australian Law Reform Commission 162, 202 civil litigation/compensation, deterrent value of 271 class actions 160-64 'access to justice', and 190, 196-7, 204-7 advantages of 205 'closed' or limited classes 161-4, 202-202, 398 common fund approach 163-4 entrepreneurial lawyers 160-61, 191-2 expense of 205 free riders, discouraging 201, 398 institutional investors, role of 193 introduction of 160, 189 opt-in approach 398
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.856 | 0.763 |
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".