Planning expansion of business operations to a new market : case: Dixu
Bibliographic record
Abstract
This thesis is commissioned by Dixu. Company was founded in 2018 and it operates in software programming and development. Company is based in Helsinki but operates around the country. \n \nDixu provides a platform, which can be used in selling or buying houses. They offer services which allows 3D interior design on any room of an apartment or a house to attract buyers with an overview on how the space could potentially look like after some fixing and redecorating. Company has partner companies such as ISKU, Keittiömaailma, Handelsbanken, Nordea, If and so forth. In addition to the ones mentioned, Dixu also uses best realtors in every area. In cooperation with partners, the company is able to provide the customers with a unique service that connects all key components regarding buying or selling a house. Using technology, Dixu is able to provide a platform for both, the customers and the partners that reduces the amount of manual work. Dixu is not a real estate firm, but instead in co-operation with some of the best realtors in the area, they aim to provide an easy an efficient platform for buying and selling homes. \n \nObjective of the thesis is to research possibilities for expanding business operations to Toronto, Canada. Even though the commissioner is not a brokerage, the field of real estate is vital in the process of buying and selling houses, so that aspect is in a significant role in this thesis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".