Correction to: Widespread yet Unreliable: A Systematic Analysis of theUse of Presence Questionnaires
Bibliographic record
Abstract
Presence, as a psychological state, is typically assessed using questionnaires. While many researchers in this field assume that these self-report instruments are standardized, the reliability of such questionnaires remains uncertain. This knowledge gap challenges the accuracy and validity of data derived from studies assessing presence. Ensuring reliable and precise data collection and reporting is essential for the credibility of findings in presence research, because inaccuracies may cause errors in conclusions, which affects theoretical understandings, methodological approaches and practical applications. To address this issue, we conducted a systematic analysis of 397 empirical quantitative studies on presence. We investigated the use of presence scales, including applications, modifications, a variety of measures and reporting practices.We found that the majority of the presence studies modify questionnaires, do not re-validate them and improperly report their methods. Based on these findings, we propose solutions to enhance transparency and validation of the presence measurements.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.140 | 0.745 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.054 | 0.016 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".