Econstellar is an open, non-commercial research platform maintained by Avishek Bhandari at the School of Humanities, Social Sciences and Management, Indian Institute of Technology Bhubaneswar. This policy explains what data the platform and its associated LinkedIn application access, how that data is used, and how to contact us. It covers the public website (avishekb9.github.io/econstellar), the compute API, and the Econstellar LinkedIn application used to publish the project's own research updates.
Public website and workbench. The website requires no account and no sign-in. We do not collect names, email addresses, or other personal information from visitors, and we do not use advertising or cross-site tracking. The only usage signal recorded is an aggregate, non-identifying count of page views and feature uses (for example, how many times a method was run), with no IP address, no cookie, and no personal identifier stored. Analyses you run on the public engine are processed transiently to return a result and are not linked to you. No page on this site asks a visitor to type an identifier: the Classroom's Market Maker exercise asks only for a display name, which never leaves the browser, as described next.
Classroom teaching pages. The Classroom modules run entirely in the visitor's own browser and have no backend. The Market Forces Lab, the Macro Vitals Lab and the Signal Room collect nothing whatsoever: they ask for no identifier, make no network request of any kind, and have no submission step. The Signal Room keeps a running score and per-concept accuracy in that browser's own local storage, where it stays; clearing the browser's site data removes it. Market Maker asks only for a display name, which, along with scores, streaks and per-concept accuracy, is held only in that browser's own local storage. Its score submission is switched off: the Submit button is shown greyed out, nothing about a run is transmitted anywhere, and the end-of-run summary is displayed on the player's own screen only. The exercise is ungraded and carries no academic weight. The pages keep an optional submission mode for instructors who adapt them for their own courses. When an instructor switches it on, a run is sent only when the student presses Submit, and only to a Google Form that instructor owns, where it is handled under Google's privacy policy; that instructor is the sole controller of the data.
LinkedIn application. The Econstellar LinkedIn application is operated solely by
the project author to publish the project's own research announcements to the author's own LinkedIn
account. Through LinkedIn's OAuth 2.0 authorisation it accesses only: the authorising user's basic
profile (name and LinkedIn member identifier) and permission to create posts on that user's behalf
(the w_member_social scope). It does not read connections, messages,
feeds, or any other LinkedIn members' data.
We do not build profiles of individuals, and we do not use any LinkedIn data for advertising or for any purpose other than publishing the operator's own posts.
The LinkedIn access token obtained during authorisation is stored securely on the operator's own server and is used only to publish content the operator initiates. It is never sold, shared, or transferred to third parties. The aggregate usage counts described above contain no personal data.
We do not sell, rent, or share personal data with third parties. Data accessed through the LinkedIn API is used strictly to provide the posting function described above, consistent with the LinkedIn API Terms of Use and the LinkedIn Platform Guidelines.
The website is hosted on GitHub Pages. Computation runs on the laboratory's own self-hosted engine rather than on a cloud provider, and the AI analyst is an open-weights model served from that same machine, so the text of a question is not sent to a third-party model provider unless a hosted-model fallback is explicitly configured on the engine. To answer a question the engine may also query public data and scholarly services: Yahoo Finance for market prices, and Crossref, OpenAlex, arXiv and FRED for references and published series. Publishing uses the LinkedIn API. Each third party named here processes requests under its own privacy policy.
You may revoke the Econstellar application's access to your LinkedIn account at any time from your LinkedIn account settings (Data privacy → Permitted services). To ask a question, or to request deletion of any data associated with you, contact us at the address below and we will respond promptly.
Econstellar is a scholarly tool and is not directed to children under the age of 16, and we do not knowingly collect data from them.
If this policy changes, the revised version will be posted on this page with a new effective date.