The Dynamics of Inferential Interpretation in Experiential Learning: Deciphering Hidden Goals from Ambiguous Experience
Bibliographic record
Abstract
According to the Carnegie School tradition of experiential learning, learning processes are driven by interpretations of experience relative to an observable goal. While prior research has considered how ambiguity may complicate interpretation, it has seldom considered how ambiguous experience emanating from the enactment of hidden goals may complicate the interpretive process. Drawing on a 13-month inductive study of CryptoTradingGroup (CTG), a distributed financial organization, and its interactions with MajorCryptoCommunity (MCC), a cryptocurrency investment community, we examine how actors engage in effective interpretation and learning when they face hidden goals and ambiguous experience. We examine how perpetrators in CTG plotted a hidden market manipulation goal in a backstage secret chatroom while simultaneously targeting MCC with invalid information enacted in the frontstage. Our analysis unpacks the dynamics of how MCC deciphered the hidden market manipulation goal and stopped the fraud through a process that we label inferential interpretation. In shifting away from a model of effective learning with statistical inference, in which interpretation is rarely examined, inferential interpretation shows how heterogeneous actors construct understandings from cues embedded in ambiguous experience during the learning process. Our study makes interpretation, i.e., the construction of meaning, central to conceptions of experiential learning when reality, causality, and intentionality are obscured.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".