Relatório Final Jogos-Simulação De Marketing – Power Computer
Bibliographic record
Abstract
This school in the course of Marketing Business Management and specifically Entrepr\nThis school in the course of Marketing Business Management and specifically\nEntrepreneurship in the discipline of Simulation - Games Marketing year was\naccordingly for the creation of a company in the computer business in business online\nsimulator called Marketplace, in order to put into practice all the theoretical knowledge\nacquired during all previous semesters.\n\nThis platform we were confronted with decisions in eight quarters corresponding 4\nevery year , in order to encourage learning in a practical way, a virtual and dynamic\nenvironment. Every quarter acareados with well organized tasks taking as a reference\npoint defined strategies such as market research analysis, branding , store management\nafter its creation , development of the policy of the 4Ps , identifying opportunities ,\nmonitoring of finances and invest heavily .\n\nAll quarters were subjected decisions and are then given the results , such as: market\nperformance , financial performance, investments in the future , the "health" of the\ncompany 's marketing efficiency then analyzed by our company , teaching and also by\ncompetition Balanced Scorecard ie , semi-annual and cumulative .\n\nFor the start of activities it was awarded the 1st year a total of 2,000,000, corresponding\nto 500,000 out of 4 first quarter , and 5,000,000 in the fifth quarter in a total of\n7,000,000 .\n\n\nThe capital invested was used to buy market research, opening sales offices , create\nbrands , contract sales force , advertise products created and perform activity R & D in\norder to make a profit and become self- sufficient to guarantee the payment of principal\ninvested to headquarters ( Corporate Headquarters ) .
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".