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Record W2107208365 · doi:10.1111/1911-3838.12024

A Conceptual Framework for Learning Management Accounting

2014· article· en· W2107208365 on OpenAlex
Gary Spraakman, Beverley Jackling

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueAccounting Perspectives · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsYork University
Fundersnot available
KeywordsHeuristicsManagement accountingHumanitiesComputer sciencePsychologyAccountingPhilosophyEconomics

Abstract

fetched live from OpenAlex

This paper demonstrates how Schoenfeld's (1985) conceptual framework for mathematics can provide an alternate framework for learning and thereby teaching management accounting. The four-part framework—heuristics, resources, beliefs, and controls—is a refinement to problem-based learning with three attributes in regard to management accounting. First, all aspects for teaching management accounting are integrated into a single framework or theory. Consistency among all parts of management accounting clarifies student and instructor roles in the learning process. Second, the framework's problem-solving focus with linkages to explanatory materials or resources allows students to be rigorously informed about the functionality of management accounting heuristics. Third, transition or extension of relatively simple, standard problems to more complex nonstandard problems or cases is facilitated by introducing appropriate beliefs and controls. In effect, this approach enables management accounting, and particularly case analysis, to be taught with more structure.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.260
Teacher spread0.246 · 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