Consulting report Team 2- MASEDI Contratistas Generales
Bibliographic record
Abstract
MASEDI is a Peruvian construction company created and founded by Vladimir Sokolic in 1999. They provide services in the field of architectural design, construction, maintenance and everything related to the area. Its approach as a company is based on leadership, experience, quality and personalized service in each project. The main problem identified is the low brand awareness which prevents from obtaining a greater number of clients in the private sector. This is mainly due to the strong competition, lack of a marketing department to promote the brand, as well as the reliance on word of mouth advertising thanks to its good reputation in the public sector. In that sense, the CEO is interested in finding strategies to make MASEDI recognized in the private sector for its quality service and expertise. The theory reviewed to develop the solution to the problem suggests marketing strategies through social media and content management; customer relationship management (CRM); and corporate social responsibility activities (CSR). Then, four alternatives were developed and evaluated regarding to cost, feasibility, effectiveness, ease of implementation and reliability. The final proposal includes an integrated marketing communication strategy through digital and nondigital media, as well as the division of the client portfolio, whose implementation plan is detailed step by step for MASEDI, in order to obtain new clients, get brand recognition and increased traffic on the web and social networks.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.359 | 0.163 |
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".