An Examination Monocular Vision Gaze Point Tracking under the Theory of 'Machines Displacing Workers' in the Philosophy of Technology
Bibliographic record
Abstract
With the continuous development of technology, we have the opportunity to quantitatively study the internal behaviors of personnel, such as attention, in a suitable way, in order to better solve the "machine compensation" for workers and achieve the development of human-machine fusion.An exploration of how varying lighting conditions directly affect the eyes and, by extension, the individual's attention has been undertaken in this study.Image sensors capture the subject's eye movements during task participation, with the location of the eye's (pupil's) center aiding in gaze point recognition and facilitating the quantification of attention.A quantifiable model of "lighting-attention" has been developed through controlled lighting conditions and replicable experimental circumstances, thereby studying the alteration of attention under different lighting and road conditions.An initial calibration of a monocular vision gaze tracking system was achieved with two commercialgrade eye trackers and calibration software, achieving a precision level of 2 cm.Nine college students aged 20-21, divided into male and female control test groups, exhibited similar attention characteristics under various lighting conditions.Overall, a negative correlation between illumination intensity and attention is observed when exceeding a certain threshold.On average, female participants maintained higher attention levels for longer durations.The efficacy of the model proposed in this study has been proven through a series of tests, providing a quantifiable reference for the mechanisms influencing attention.So it can provide the basis for the study of the benefits of factory workers.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".