New Capital Relocation of Indonesia: Estimating Food Demand in East Kalimantan and Jakarta
Bibliographic record
Abstract
This research examines the current state of food demand and its influencing factors in East Kalimantan and Jakarta, considering the relocation of Indonesia’s capital city. Using the Quadratic Almost Ideal Demand System (QUAIDS) method and 2021 Susenas data, we have analyzed the impact of variations in food prices, income, and demographic variables on seven food categories—rice, grains, tubers, legumes, animal protein, fruit & vegetables, and prepared food. Our findings reveal that increased income increases demand for animal protein and fruit & vegetables in East Kalimantan but decreases in Jakarta. In both regions, as expenditure increases, rice consumption decreases while the demand for prepared food increases. An increase of 1% in rice prices will reduce rice consumption by approximately 0.334-0.487% in East Kalimantan and 0.126-0.202% in Jakarta. Households in East Kalimantan consume more prepared food when prices for other food items increase. In Jakarta, prepared food consumption decreases as rice prices go up. Demographic factors play a crucial role in determining food demand. For example, public sector employees in East Kalimantan consume more rice and less prepared food, while in Jakarta, they prefer prepared food and consume less rice. It is essential to pay adequate attention to the demand for food for public workers who will move to East Kalimantan and the demographic factors that influence it. This consideration will ensure that the residents of East Kalimantan and those who relocate to the new capital receive the necessary food provisions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".