Development of a digital photo hoarding scale: A research with undergraduate students
Bibliographic record
Abstract
This study focuses on hoarding of digital assets. Today people's ownership of digital assets can be uncontrolled and the measurement tool designed in the study is expected to be useful for young people, health care organizations, businesses (smartphone firms etc.) and researchers. In the study, previous researches on hoarding, in particular hoarding of digital assets are reviewed. We then describe the process by which we developed our digital photograph hoarding scale (DPHS): development of scale items, evaluation of items, testing of a preliminary version, conducting validity and reliability analyses and analysis of scale scores. As a result, sub-dimensions of the digital photograph hoarding are identified as: problems caused by uncontrolled acquisition of photographs; problems caused by clutter; uncontrolled clutter of photographs; failure to dispose of photographs and related problems; uncontrolled taking of photographs accompanied by a constant desire to do so. It is seen that people with high DPHS scores also have higher scores on measures of photographing and photograph examination. Finally, the limitations of the research are discussed and suggestions for the future researches are offered.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".