MétaCan
Menu
Back to cohort
Record W7058016266

Maritime Bus: Goal setting and surviving the pandemic crisis [Case study and teaching notes]

2023· other· en· W7058016266 on OpenAlexaboutno aff

Bibliographic record

VenueMiddlesex University Research Repository (Middlesex University Of London) · 2023
Typeother
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionTSG101Gestational periodHyporeflexiaArticular cartilage damagePretext
DOInot available

Abstract

fetched live from OpenAlex

Maritime Bus is a case study focusing on entrepreneurship; strategy; goal setting and decision-making during the global pandemic crisis. The entrepreneuer in this case is faced with navigating a busing company in Atlantic Canada through declining ridership, mounting financial pressures and contemplating laying off staff. This case will certainly challenge and motivate your undergraduate or graduate students.
\n
\nThe teaching note outlines the corresponding "Maritime Bus Goal Setting and Surviving the Pandemic Crisis." The purpose of a case study teaching note is to provide educators with a comprehensive and detailed guide on how to effectively use a specific case study in the classroom. It serves as a roadmap for instructors, offering insights into the case's objectives, key themes, and potential teaching strategies. The teaching note outlines the case's background information, identifies important teaching points, and suggests discussion questions, analysis frameworks, and recommended resources. It also assists instructors in managing classroom dynamics, addressing potential challenges, and facilitating meaningful student engagement. Ultimately, the teaching note aims to enhance the learning experience by empowering educators to effectively navigate and leverage the case study material to promote critical thinking, problem-solving, and application of concepts.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.025
GPT teacher head0.252
Teacher spread0.227 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueMiddlesex University Research Repository (Middlesex University Of London)Same topicMagnetic Field Sensors TechniquesFrench-language works237,207