TAP Air Portugal : adaptive strategies due to the pandemic of Covid19
Bibliographic record
Abstract
This thesis is going to be presented in the form of a case study. The main goal is to study how TAP Air Portugal adapted its core business in response to the pandemic of Covid19. Furthermore, the case will also explore important topics such as what are adaptive strategies, the concept of competitive advantage and the ability to understand environmental and consumer habits changes. TAP Air Portugal had to make some important and crucial decisions that have had an influence in the way its business works and that will be highlighted in this case. Adding to it, TAP Air Portugal adaptive strategies will be compared to the entire industry. This comparison is crucial to understand the entire market and if the strategies taken by TAP Air Portugal were the more suitable ones. In order to have a deeper knowledge regarding the strategies taken by TAP Air Portugal, an interview has been conducted with the Marketing Manager of the company, Dr. Paula Canada Adding to the case study, there will also be developed some theorical concepts that may come as a hand to better understand the case study. The goal here is to provide the tools and all the necessary material to have complete knowledge of the case. Giving suggestions, personal opinion and any type of explanation will be given as a conclusion, after the elaboration of the case study and the theorical 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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".