Bibliographic record
Abstract
Bigmond is a Peruvian company dedicated to the headhunting services and human \nresources consulting. In the last months, the company has been facing a decrease on its \ncommercial activities due to the fierce competition and, more recently, due to the COVID-19 \npandemic. This situation has aware Bigmond of the necessity of reinvent an offer a service \nthat target individuals rather than companies, this is why an outplacement service was thought \nas a suitable solution. Bigond also looks at the service as a way to keep its reputations as a \nanti-discriminatory company and expects that this new service could be offered to low and \nmiddle management job positions. Bigmond wants to achieve its objective by launching the \nservice in the short-term but need a clear path of how to do it. The present thesis is intented to \ngive Bigmond a detailed study with the best practices of how to implement the service. The \nthesis starts with an analysis of the Porter’s five forces and an overview of the external and \ninternal factors affecting the company. Then, a literature review is presented in order to give a \nclear understanding of what is outplacement and its implications. Next, a benchmark of \ninternational and national companies that are currently offering the service and a survey were \ndeveloped as the qualitative and quantitative analysis respectively. As result, a new business \nunit with a fully digital service through a platform was defined as the best alternative to \nimplement. The project was estimated to last 76 working days and to have an initial cost of S/. \n181,000.00. Finally, as outcomes, the projections showed that the company can achieve \npositive results in the first year after launching the service and to get positive reputational and \nbrand awareness outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.266 | 0.092 |
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".