Determining the Verisimilitude of Competing Theoretical Explanations in Letter Case Processing and the Prevention of Type III and IV Errors in Experimental Psychology
Bibliographic record
Abstract
The aim of this thesis was twofold.First, a cognitive experiment was conducted to determine which hypothesis explained the letter case processing of words best.N = 173 university students were recruited to participate in a word/non-word lexical decision study, which followed a 2 (case type: lowercase or uppercase) × 2 (attentive state: focused or mind-wandering) × 3 (orthographic neighbourhood size (N): zero, low, or high) repeated measures design.The effects of this design's model were examined in two multiverse analyses to account for different model, measurement, covariate, and participant exclusion decisions that may impact the results of the study.A significant interaction between case type and N was found across specifications in both multiverses.From this finding, it was concluded that the case invariance hypothesis stands its ground, but that its auxiliary assumptions are put into question.Second, the current work attempted to propose a method for operationalizing Type III and IV errors to determine how they can be prevented in practice.It was found that operationalization would not be possible without adequate classification of error kinds.Hence, a provisional class distinction was proposed in the current work, from which the function of error prevention was introduced with an example from the current study.
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 | 0.034 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".