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Record W4241113690 · doi:10.17771/pucrio.acad.9048

A RETENÇÃO DE CLIENTES NO SETOR DE ENSINO: ESTUDO DE CASO EM UM CURSO DE INGLÊS

2006· dissertation· pt· W4241113690 on OpenAlexaff
ADRIANA LUCIA DE ESCOBAR CHAVES

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

no curso de Pós-graduação em Metodologia do Ensino da Língua Inglesa, pela Universidade de Cambridge, Inglaterra, 1994.Diplomada no curso de Pós-graduação do Management pelo IAG da Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) em 2003.Atualmente é gerente de filial da Cultura Inglesa do Rio de Janeiro, responsável por toda a operação pedagógica, administrativa e financeira daquela unidade.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.004
Scholarly communication0.0080.005
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.046
GPT teacher head0.353
Teacher spread0.307 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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