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Record W2170317670 · doi:10.1177/0956797611404899

Stuck in the Middle

2011· article· en· W2170317670 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Science · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPsychologyMonotonic functionCognitive psychologyState (computer science)Value (mathematics)Social psychologyComputer scienceMathematicsStatisticsMathematical analysisAlgorithm

Abstract

fetched live from OpenAlex

The classic goal-gradient hypothesis posits that motivation to reach a goal increases monotonically with proximity to the desired end state. However, we argue that this is not always the case. In this article, we show that motivation to engage in goal-consistent behavior can be higher when people are either far from or close to the end state and lower when they are about halfway to the end state. We propose a psychophysical explanation for this tendency to get "stuck in the middle." Building on the assumption that motivation is influenced by the perceived marginal value of progress toward the goal, we show that the shape of the goal gradient varies depending on whether an individual monitors progress in terms of distance from the initial state or from the desired end state. Our psychophysical model of goal pursuit predicts a previously undiscovered nonmonotonic gradient, as well as two monotonic gradients.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.507
GPT teacher head0.510
Teacher spread0.003 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it