Economic Development Planning in Medan City
Bibliographic record
Abstract
Economic development in an area must go through several plans and targets both long term and short term. The process leading to the realization of some of these targets is useful for conditioning the capacity for increasing the economy of the community. One of the characteristics of society that has gone hand in hand with economic development is marked by the widespread use of technology and changes in a more positive direction from the mindset of the people to institutional institutions. The research method used as an analytical tool in this study is a descriptive type with the aim of obtaining a projected representation of a participation and procedural regulatory mechanism in the community during the process of preparing the RPJMD in Medan City from 2010 to 2025. The analytical steps used are secondary data or data. supporting information in the form of a collection of information regarding the results of interviews with respondents as well as providing an appropriate presentation with an explanation of the matrix accompanied by the presentation that has been displayed by the researcher. The variety of informants or respondents used as a collection of information in this study involved various elements such as social organizations, elements from the Medan city government to academics under the auspices of universities to the mass media and the main element is the Medan City DPRD. The process of compiling the results of this research is oriented towards a question and answer process or interviews by applying qualitative analysis steps with the aim of criticizing various phenomena in the process of preparing reports and investigating phenomena regarding the research topic. The continuation of the process is a data analysis technique using logical interpretation supported by previously collected data. The results of this study are that in development planning, Medan City has a challenge by having to handle an area of 265.10 km2, with an estimated population density of 9,836.56 people/km2 in 2025. This population increase can be an obstacle in handling development planning., however This is also an opportunity if the government can take advantage of this population increase. For population growth itself in 2025 there will be a decline, which in 2020 is expected to grow by 1.03 to 0.94% in 2025.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".