EEG-Neurofeedback and the Correction of Misleading Information: A Reply to Pigott and Colleagues
Bibliographic record
Abstract
As scientists, we gladly welcome academic debate surrounding our research. However, when commentators Pigott and colleagues contact our universities demanding that we (RTT and AR) be reprimanded and claiming that we are “contaminating the scientific literature with [our] animus-driven venom” and should “(re)take Learning 101 before publishing further”, their arguments cease to hold scholarly appeal. The three directors at McGill University in receipt of the accusations put forward by Pigott and colleagues discussed the complaint and all agreed that it did not merit a response. Chapman University launched an assessment and an administrator at the level of Dean performed the evaluation and dismissed the complaint. Pigott and colleagues are willfully tying-up academic resources in an attempt to stifle scientific research that challenges their opinions.[...]
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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 | 0.025 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.074 | 0.104 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".