Bibliographic record
Abstract
Bigmond is a Peruvian company dedicated to the headhunting services and human resources consulting. In the last months, the company has been facing a decrease on its commercial activities due to the fierce competition and, more recently, due to the COVID-19 pandemic. This situation has aware Bigmond of the necessity of reinvent an offer a service that target individuals rather than companies, this is why an outplacement service was thought as a suitable solution. Bigond also looks at the service as a way to keep its reputations as a anti-discriminatory company and expects that this new service could be offered to low and middle management job positions. Bigmond wants to achieve its objective by launching the service in the short-term but need a clear path of how to do it. The present thesis is intented to give Bigmond a detailed study with the best practices of how to implement the service. The thesis starts with an analysis of the Porter’s five forces and an overview of the external and internal factors affecting the company. Then, a literature review is presented in order to give a clear understanding of what is outplacement and its implications. Next, a benchmark of international and national companies that are currently offering the service and a survey were developed as the qualitative and quantitative analysis respectively. As result, a new business unit with a fully digital service through a platform was defined as the best alternative to implement. The project was estimated to last 76 working days and to have an initial cost of S/. 181,000.00. Finally, as outcomes, the projections showed that the company can achieve positive results in the first year after launching the service and to get positive reputational and brand awareness outcomes.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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; both teacher heads agree on what is shown here.
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".