Bibliographic record
Abstract
When we were first invited to write an essay on the use of PLS for CAIS, we wanted to focus on recent developments to help applied IS researchers, and the CAIS community of authors, reviewers, and editors make use of the latest research on and methodological advances in PLS. Recognizing that Information Systems is arguably the discipline in which the use of PLS as an alternative to CB-SEM originated and is most widely used, we realized that, pragmatically, our essay must focus on how to use PLS, not whether to use PLS.\nWe received six interesting responses from researchers active in the PLS community. Their thoughts on our presentation of recent developments in PLS show very different perspectives with many points of difference amongst points of agreement in all the responses. In this rejoinder, we briefly respond to the received comments, clarify our position and ideas, and identify points of agreement (and disagreement). We emphasize that none of the responses give us cause to revise or eliminate our recommendations in recent developments.\nOverall, we believe this discussion on PLS to be valuable in advancing the use of PLS in Information Systems, which is, as we show in this rejoinder, an urgent issue.
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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 | 0.055 | 0.249 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.031 | 0.057 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".