Measuring Shyness in Children Online
Bibliographic record
Abstract
During the pandemic, developmental psychology research labs had to adapt to virtual testing methods. Many labs continue to conduct online studies even as restrictions have been lifted, as these methods extend the geographical range of participating families. However, online testing methods cannot always match in-person procedures. For example, psychologists often measure behaviours that indicate shyness in children during in-person laboratory visits that include contexts such as giving a public speech or interacting with confederates. To adapt this method to a virtual environment, Dr. MacGowan has recently innovated a new procedure for studying shyness within an online, synchronous testing session. Dr. MacGowan's ongoing eProsocial study evaluates children’s behavioral shyness during synchronous sessions on Zoom. This is accomplished by having an experimenter posed as a child confederate named “Sam” log on with a gender-matched profile picture of a child; participants are told that Sam is the next child scheduled for a testing session. The lead experimenter leaves the participant to “chat” with Sam and the disguised researcher uses 7 pre-recorded audio clips of a real child’s voice to facilitate conversations with the participant (e.g., “What grade are you in?”). When the lead experimenter returns, they perform a manipulation check to gauge if the child is suspicious of Sam’s existence by asking “What did you think of Sam?”. A total of 194 sessions have been conducted to date, and only one participant has indicated doubt in the existence of Sam during manipulation checks. These results exhibit initial construct validity. To further examine validity, research assistants will code shyness behaviours, such as latency to respond, and compare this data to caregiver shyness reports. Dr MacGowan’s approach to measuring shyness in an online setting is promising, and other developmental researchers should consider replicating this paradigm in future studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".