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Record W2771103202

Plan de negocios para la exportación de tejidos artesanales ecuatorianos a Toronto y Otawa-Canadá

2017· dissertation· es· W2771103202 on OpenAlexaboutno aff
Larrea Flores, Daniela Carolina

Bibliographic record

Venuenot available
Typedissertation
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsClothingBusinessCapital (architecture)Textile industryProduction (economics)Product (mathematics)TextileCommerceAdvertisingGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

By 2014 the textile industry in Ecuador had grown by a 4.3 percent, which represented a 0.9 percent of the countrys GDP and a 7.24 percent of the manufacturing GDP. Exports have been affected by tariffs on certain capital goods needed for the production of garments and clothing accessories. On the other hand, the high demand of garments and clothing accessories by Canada since 2011 has led the countrys textile industry to decrease by a 4 percent annually, leading that the imports of these products increased by 2015 to a total value of 12,500 million dollars, this is due because the production of garment and clothing accessories in Canada is focused on the elaboration of high end clothing and with high quality. That is why, by the time of the investigation of the external factors in Ecuador and in Canada, studying the behavior of potential clients using quantitative and qualitative techniques and seeing that there is a business opportunity, the company Sapi was born. Sapi exports handcrafted fabrics to Toronto and Ottawa, such as scarves and beanies which are made with alpaca wool, these accessories are made by qualified artisans from Imbabura province in Ecuador, and it is focused on selling to men and women from 18 to 40 years old of a middle and upper high socioeconomic level who like to have foreign crafts, accessories and fabrics because of the culture these represent. The marketing strategy to be used is a differentiation strategy, because of the way the accessories are made, the designs they have and the raw material that is going to be used in the elaboration. The initial investment for the project is of $18 271, 20 which 50 percent is going to be financed with personal capital and the other 50 percent with a loan in a term of five years, to identify if the project if profitable the NPV is of $57 183, 21 an the IRR of 81.49 percent the recovery period starts in the third year.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.254
Teacher spread0.216 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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