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Record W6967549748 · doi:10.5281/zenodo.11483200

Satisfaction with the Services Provided by the Bureau of Fire Protection

2024· article· en· W6967549748 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Systems and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Quality (philosophy)Fire protectionDescriptive statisticsCustomer satisfactionGovernment (linguistics)

Abstract

fetched live from OpenAlex

Abstract: In this context, this study aimed to determine the level of satisfaction with the services provided by the BFP in Bacolod City, Negros Occidental for the First Quarter of 2024. This information can guide improvements and help the BFP better meet community needs. Data needed for this descriptive research study was collected from 100 respondents using a self-made data-gathering instrument that has passed the stringent tests of validity and reliability. Overall, the respondents are commonly composed of younger generations, commonly female and with the micro-enterprise. Corresponding results surfaced that an outstanding level of respondents’ satisfaction was shown when grouped according to three primary variables, which are age, sex, and number of employees; as such, areas of the BFP services, emergency response services, and investigative and compliance services were at a very high level. Conversely, areas of preventive services entail a need for improvement. Other than that, the result shows no significant difference in the level of satisfaction with the services provided by the BFP when grouped according to the abovementioned variables. The study results call for all BFP personnel to actively implement and enhance the skills needed to be proactive in spreading fire safety awareness and preventive measures for a fire-safe nation. Keywords: BFP, satisfaction level, fire and non-fire services, Bacolod City

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.026
GPT teacher head0.254
Teacher spread0.229 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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