Future-Oriented Thinking: Saving, Prospective Memory, and Planning in Young Children
Bibliographic record
Abstract
Saving is an important future-oriented thinking skill to acquire but little is known about how its early development relates to other future-oriented thinking abilities.The present study would have examined whether saving ability is related to prospective memory (PM) and planning, two other aspects of future-oriented thinking, during the preschool years, as little is known about the relation between saving and PM.Due to the SARS-CoV-2 pandemic, data collection could not occur.However, approximately 80 participants would have been recruited from daycares in Moncton, New Brunswick and the surrounding area.Four-and five-year-old children would have completed a token and sticker saving task, a card-sorting and naturalistic PM task, and the Tower of Hanoi and Truck Loading planning task, as well as a vocabulary measure (abbreviated PPVT-V).Potential results are discussed regarding what may have been found should data collection have been able to occur.iii *If "green/stickers" → That's right, you will get the green tray with the stickers game.*If "blue/toys" → No, you will get the green tray with the stickers game.*If other response→ You will get the green tray with the stickers game.*If incorrect, repeat memory question Okay, so which game will I put on the table first?Correct Incorrect Don't Know No Answer *If incorrect again, correct and continue Question B Okay, so which game will I put on the table after that?Correct Incorrect Don't Know No Answer *If "toys/blue" → That's right, you will get the blue tray with the toys game.*If "green/stickers" → No, you will get the blue tray with the toys game.*If other response → You will get the blue tray with the toys game.*If incorrect, repeat memory question Okay, so which game will I put on the table after that?Correct Incorrect Don't Know No Answer *If incorrect again, correct and continue Question C Now, how do you get a sticker in this game?Correct Incorrect Don't Know No Answer *If correct (e.g., put a token into the green box)→ That's right, you put a token in the green box to get a sticker.*If incorrect response → No, you will put a token in the green box to get a sticker.*If other response → You put a token in the green box to get a sticker.*If incorrect, repeat memory question Now, how do you get a sticker in this game?Correct Incorrect Don't Know No Answer *If incorrect again, correct and continue Question D Now, how do you get a toy in this game?Correct Incorrect Don't Know No Answer *If correct (e.g., put a token into the blue box) → That's right, you put a token in the blue box to get a toy.*If incorrect response → No, you put a token in the blue box to get a toy.*If other response → You put a token in the blue box to get a toy.*If incorrect, repeat memory question Now, how do you get a toy in this game?Correct Incorrect Don't Know No Answer *If incorrect again, correct and continue
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".