Análise estratégica na gestão de pequenas empresas de prestação de serviços : proposição de um modelo no setor hoteleiro de São Carlos
Bibliographic record
Abstract
The main objective of this research is to adapt and improve a route of strategic planning, elaborated for small industrial companies, thro ugh its appl ication in small hotel companies.The strategic planning has been little spread in small companies.T he small Brazilian companies do not have a culture o f strategic planning and states that the lack of strategic planning in small companies implicates in an inefficiency in their making of strategic decisions.The that small hotel companies are characterized by the lack of use of strategic planning in the ir making of dec isions.The appl ication methodology to be developed w ill be research-exploratory.It consists of a category of qualitative research, whose study objective is to describe, register, ana lyze and interpret the phenomena in a rder to know the ir present operation.To reach the objectives proposed in thi s research a fi eld work was deve loped in four hote ls São Carlos (SP) a result the systematization a route of strategic planning is for the section of hotel services.Thus, the importa nce of this researc h lies upon the academic contribution that it intends to offer to researchers, executives belonging to the lodging branch and other inte rested institutions in the subject, in the sense o f promoting a larger understanding o f the practices of applied strateg ic planning in small hotel compani es.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".